1
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Murphy C, Thibeault V, Allard A, Desrosiers P. Duality between predictability and reconstructability in complex systems. Nat Commun 2024; 15:4478. [PMID: 38796449 PMCID: PMC11127975 DOI: 10.1038/s41467-024-48020-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/15/2024] [Indexed: 05/28/2024] Open
Abstract
Predicting the evolution of a large system of units using its structure of interaction is a fundamental problem in complex system theory. And so is the problem of reconstructing the structure of interaction from temporal observations. Here, we find an intricate relationship between predictability and reconstructability using an information-theoretical point of view. We use the mutual information between a random graph and a stochastic process evolving on this random graph to quantify their codependence. Then, we show how the uncertainty coefficients, which are intimately related to that mutual information, quantify our ability to reconstruct a graph from an observed time series, and our ability to predict the evolution of a process from the structure of its interactions. We provide analytical calculations of the uncertainty coefficients for many different systems, including continuous deterministic systems, and describe a numerical procedure when exact calculations are intractable. Interestingly, we find that predictability and reconstructability, even though closely connected by the mutual information, can behave differently, even in a dual manner. We prove how such duality universally emerges when changing the number of steps in the process. Finally, we provide evidence that predictability-reconstruction dualities may exist in dynamical processes on real networks close to criticality.
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Affiliation(s)
- Charles Murphy
- Département de physique, de génie physique et d'optique, Université Laval, Québec, QC, G1V 0A6, Canada.
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, G1V 0A6, Canada.
| | - Vincent Thibeault
- Département de physique, de génie physique et d'optique, Université Laval, Québec, QC, G1V 0A6, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Antoine Allard
- Département de physique, de génie physique et d'optique, Université Laval, Québec, QC, G1V 0A6, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Patrick Desrosiers
- Département de physique, de génie physique et d'optique, Université Laval, Québec, QC, G1V 0A6, Canada.
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, G1V 0A6, Canada.
- Centre de recherche CERVO, Québec, QC, G1J 2G3, Canada.
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2
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Touwen L, Bucur D, van der Hofstad R, Garavaglia A, Litvak N. Learning the mechanisms of network growth. Sci Rep 2024; 14:11866. [PMID: 38789498 PMCID: PMC11126688 DOI: 10.1038/s41598-024-61940-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 05/11/2024] [Indexed: 05/26/2024] Open
Abstract
We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.
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Affiliation(s)
- Lourens Touwen
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands
| | - Doina Bucur
- Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands
| | - Remco van der Hofstad
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands
| | - Alessandro Garavaglia
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands
| | - Nelly Litvak
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands.
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3
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Kim E, Kim Y, Jin H, Lee Y, Lee H, Lee S. The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data. Front Public Health 2024; 12:1386495. [PMID: 38827618 PMCID: PMC11140122 DOI: 10.3389/fpubh.2024.1386495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
Introduction Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. Methods The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Results Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. Discussion This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.
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Affiliation(s)
- Eunmi Kim
- Institute of Mathematical Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Yunhwan Kim
- College of General Education, Kookmin University, Seoul, Republic of Korea
| | - Hyeonseong Jin
- Department of Mathematics, Jeju National University, Jeju, Republic of Korea
| | - Yeonju Lee
- Division of Applied Mathematical Sciences, Korea University—Sejong, Sejong, Republic of Korea
| | - Hyosun Lee
- Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
| | - Sunmi Lee
- Applied Mathematics, Kyung Hee University, Yongin, Republic of Korea
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4
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Gao X, Xu Y. Markovian Approach for Exploring Competitive Diseases with Heterogeneity-Evidence from COVID-19 and Influenza in China. Bull Math Biol 2024; 86:71. [PMID: 38719993 DOI: 10.1007/s11538-024-01300-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/19/2024] [Indexed: 05/23/2024]
Abstract
Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China.
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Affiliation(s)
- Xingyu Gao
- School of Mathematics and Statistics, Changshu Institute of Technology, Changshu, 215500, China.
| | - Yuchao Xu
- GE HealthCare Technologies Inc, No. 1 Huatuo Road, Shanghai, 201210, China
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5
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Xia C, Johnson NF. Nonlinear spreading behavior across multi-platform social media universe. CHAOS (WOODBURY, N.Y.) 2024; 34:043149. [PMID: 38648381 DOI: 10.1063/5.0199655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024]
Abstract
Understanding how harmful content (mis/disinformation, hate, etc.) manages to spread among online communities within and across social media platforms represents an urgent societal challenge. We develop a non-linear dynamical model for such viral spreading, which accounts for the fact that online communities dynamically interconnect across multiple social media platforms. Our mean-field theory (Effective Medium Theory) compares well to detailed numerical simulations and provides a specific analytic condition for the onset of outbreaks (i.e., system-wide spreading). Even if the infection rate is significantly lower than the recovery rate, it predicts system-wide spreading if online communities create links between them at high rates and the loss of such links (e.g., due to moderator pressure) is low. Policymakers should, therefore, account for these multi-community dynamics when shaping policies against system-wide spreading.
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Affiliation(s)
- Chenkai Xia
- Physics Department, George Washington University, Washington DC 20052, USA
| | - Neil F Johnson
- Physics Department, George Washington University, Washington DC 20052, USA
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6
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Wang W, Chen G, Wong EWM. Delay-driven phase transitions in an epidemic model on time-varying networks. CHAOS (WOODBURY, N.Y.) 2024; 34:043146. [PMID: 38639346 DOI: 10.1063/5.0179068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 03/29/2024] [Indexed: 04/20/2024]
Abstract
A complex networked system typically has a time-varying nature in interactions among its components, which is intrinsically complicated and therefore technically challenging for analysis and control. This paper investigates an epidemic process on a time-varying network with a time delay. First, an averaging theorem is established to approximate the delayed time-varying system using autonomous differential equations for the analysis of system evolution. On this basis, the critical time delay is determined, across which the endemic equilibrium becomes unstable and a phase transition to oscillation in time via Hopf bifurcation will appear. Then, numerical examples are examined, including a periodically time-varying network, a blinking network, and a quasi-periodically time-varying network, which are simulated to verify the theoretical results. Further, it is demonstrated that the existence of time delay can extend the network frequency range to generate Turing patterns, showing a facilitating effect on phase transitions.
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Affiliation(s)
- Wen Wang
- School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China
| | - Guanrong Chen
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
| | - Eric W M Wong
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
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7
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Um J, Hong H, Park H. Validity of annealed approximation in a high-dimensional system. Sci Rep 2024; 14:6816. [PMID: 38514701 PMCID: PMC10957964 DOI: 10.1038/s41598-024-57323-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/18/2024] [Indexed: 03/23/2024] Open
Abstract
This study investigates the suitability of the annealed approximation in high-dimensional systems characterized by dense networks with quenched link disorder, employing models of coupled oscillators. We demonstrate that dynamic equations governing dense-network systems converge to those of the complete-graph version in the thermodynamic limit, where link disorder fluctuations vanish entirely. Consequently, the annealed-network systems, where fluctuations are attenuated, also exhibit the same dynamic behavior in the thermodynamic limit. However, a significant discrepancy arises in the incoherent (disordered) phase wherein the finite-size behavior becomes critical in determining the steady-state pattern. To explicitly elucidate this discrepancy, we focus on identical oscillators subject to competitive attractive and repulsive couplings. In the incoherent phase of dense networks, we observe the manifestation of random irregular states. In contrast, the annealed approximation yields a symmetric (regular) incoherent state where two oppositely coherent clusters of oscillators coexist, accompanied by the vanishing order parameter. Our findings imply that the annealed approximation should be employed with caution even in dense-network systems, particularly in the disordered phase.
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Affiliation(s)
- Jaegon Um
- Department of Physics, Pohang University of Science and Technology, Pohang, 37673, South Korea
| | - Hyunsuk Hong
- Department of Physics and Research Institute of Physics and Chemistry, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Hyunggyu Park
- Quantum Universe Center, Korea Institute for Advanced Study, Seoul, 02455, South Korea
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8
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Kwon S, Park JM. General protocol for predicting outbreaks of infectious diseases in social networks. Sci Rep 2024; 14:5973. [PMID: 38472283 DOI: 10.1038/s41598-024-56340-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 03/05/2024] [Indexed: 03/14/2024] Open
Abstract
Epidemic spreading on social networks with quenched connections is strongly influenced by dynamic correlations between connected nodes, posing theoretical challenges in predicting outbreaks of infectious diseases. The quenched connections introduce dynamic correlations, indicating that the infection of one node increases the likelihood of infection among its neighboring nodes. These dynamic correlations pose significant difficulties in developing comprehensive theories for threshold determination. Determining the precise epidemic threshold is pivotal for diseases control. In this study, we propose a general protocol for accurately determining epidemic thresholds by introducing a new set of fundamental conditions, where the number of connections between individuals of each type remains constant in the stationary state, and by devising a rescaling method for infection rates. Our general protocol is applicable to diverse epidemic models, regardless of the number of stages and transmission modes. To validate our protocol's effectiveness, we apply it to two widely recognized standard models, the susceptible-infected-recovered-susceptible model and the contact process model, both of which have eluded precise threshold determination using existing sophisticated theories. Our results offer essential tools to enhance disease control strategies and preparedness in an ever-evolving landscape of infectious diseases.
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Affiliation(s)
- Sungchul Kwon
- Department of Physics, The Catholic University of Korea, Bucheon, 14662, Korea
| | - Jeong-Man Park
- Department of Physics, The Catholic University of Korea, Bucheon, 14662, Korea.
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9
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Wang G, Yao W. An application of small-world network on predicting the behavior of infectious disease on campus. Infect Dis Model 2024; 9:177-184. [PMID: 38261962 PMCID: PMC10797140 DOI: 10.1016/j.idm.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/14/2023] [Accepted: 12/25/2023] [Indexed: 01/25/2024] Open
Abstract
Networks haven been widely used to understand the spread of infectious disease. This study examines the properties of small-world networks in modeling infectious disease on campus. Two different small-world models are developed and the behaviors of infectious disease in the models are observed through numerical simulations. The results show that the behavior pattern of infectious disease in a small-world network is different from those in a regular network or a random network. The spread of the infectious disease increases as the proportion of long-distance connections p increasing, which indicates that reducing the contact among people is an effective measure to control the spread of infectious disease. The probability of node position exchange in a network (p2) had no significant effect on the spreading speed, which suggests that reducing human mobility in closed environments does not help control infectious disease. However, the spreading speed is proportional to the number of shared nodes (s), which means reducing connections between different groups and dividing students into separate sections will help to control infectious disease. In the end, the simulating speed of the small-world network is tested and the quadratic relationship between simulation time and the number of nodes may limit the application of the SW network in areas with large populations.
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Affiliation(s)
- Guojin Wang
- School of Management, Fudan University, 220 Handan Road, Shanghai, 200433, China
| | - Wei Yao
- Shanghai Key Laboratory of Acupuncture Mechanism and Acupoint Function, Fudan University, 220 Handan Road, Shanghai, 200433, China
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10
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Ding Y, Gao J, Magdon-Ismail M. Efficient parameter inference in networked dynamical systems via steady states: A surrogate objective function approach integrating mean-field and nonlinear least squares. Phys Rev E 2024; 109:034301. [PMID: 38632807 DOI: 10.1103/physreve.109.034301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 01/08/2024] [Indexed: 04/19/2024]
Abstract
In networked dynamical systems, inferring governing parameters is crucial for predicting nodal dynamics, such as gene expression levels, species abundance, or population density. While many parameter estimation techniques rely on time-series data, particularly systems that converge over extreme time ranges, only noisy steady-state data is available, requiring a new approach to infer dynamical parameters from noisy observations of steady states. However, the traditional optimization process is computationally demanding, requiring repeated simulation of coupled ordinary differential equations. To overcome these limitations, we introduce a surrogate objective function that leverages decoupled equations to compute steady states, significantly reducing computational complexity. Furthermore, by optimizing the surrogate objective function, we obtain steady states that more accurately approximate the ground truth than noisy observations and predict future equilibria when topology changes. We empirically demonstrate the effectiveness of the proposed method across ecological, gene regulatory, and epidemic networks. Our approach provides an efficient and effective way to estimate parameters from steady-state data and has the potential to improve predictions in networked dynamical systems.
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Affiliation(s)
- Yanna Ding
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| | - Jianxi Gao
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| | - Malik Magdon-Ismail
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
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11
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Persoons R, Sensi M, Prasse B, Van Mieghem P. Transition from time-variant to static networks: Timescale separation in N-intertwined mean-field approximation of susceptible-infectious-susceptible epidemics. Phys Rev E 2024; 109:034308. [PMID: 38632755 DOI: 10.1103/physreve.109.034308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/15/2024] [Indexed: 04/19/2024]
Abstract
We extend the N-intertwined mean-field approximation (NIMFA) for the susceptible-infectious-susceptible (SIS) epidemiological process to time-varying networks. Processes on time-varying networks are often analyzed under the assumption that the process and network evolution happen on different timescales. This approximation is called timescale separation. We investigate timescale separation between disease spreading and topology updates of the network. We introduce the transition times [under T]̲(r) and T[over ¯](r) as the boundaries between the intermediate regime and the annealed (fast changing network) and quenched (static network) regimes, respectively, for a fixed accuracy tolerance r. By analyzing the convergence of static NIMFA processes, we analytically derive upper and lower bounds for T[over ¯](r). Our results provide insights and bounds on the time of convergence to the steady state of the static NIMFA SIS process. We show that, under our assumptions, the upper-transition time T[over ¯](r) is almost entirely determined by the basic reproduction number R_{0} of the network. The value of the upper-transition time T[over ¯](r) around the epidemic threshold is large, which agrees with the current understanding that some real-world epidemics cannot be approximated with the aforementioned timescale separation.
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Affiliation(s)
- Robin Persoons
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
| | - Mattia Sensi
- MathNeuro Team, Inria at Université Côte d'Azur, 2004 Rte des Lucioles, 06410 Biot, France
- Department of Mathematical Sciences "G. L. Lagrange", Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Bastian Prasse
- European Centre for Disease Prevention and Control (ECDC), Gustav III's Boulevard 40, 169 73 Solna, Sweden
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands
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12
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Papo D, Buldú JM. Does the brain behave like a (complex) network? I. Dynamics. Phys Life Rev 2024; 48:47-98. [PMID: 38145591 DOI: 10.1016/j.plrev.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/27/2023]
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.
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Affiliation(s)
- D Papo
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy; Center for Translational Neurophysiology, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy.
| | - J M Buldú
- Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, Madrid, Spain
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13
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Yan Z, Gao J, Lan Y, Xiao J. Bridge synergy and simplicial interaction in complex contagions. CHAOS (WOODBURY, N.Y.) 2024; 34:033118. [PMID: 38457849 DOI: 10.1063/5.0165572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/17/2024] [Indexed: 03/10/2024]
Abstract
Modeling complex contagion in networked systems is an important topic in network science, for which various models have been proposed, including the synergistic contagion model that incorporates coherent interference and the simplicial contagion model that involves high-order interactions. Although both models have demonstrated success in investigating complex contagions, their relationship in modeling complex contagions remains unclear. In this study, we compare the synergy and the simplest form of high-order interaction in the simplicial contagion model, known as the triangular one. We analytically show that the triangular interaction and the synergy can be bridged within complex contagions through the joint degree distribution of the network. Monte Carlo simulations are then conducted to compare simplicial and corresponding synergistic contagions on synthetic and real-world networks, the results of which highlight the consistency of these two different contagion processes and thus validate our analysis. Our study sheds light on the deep relationship between the synergy and high-order interactions and enhances our physical understanding of complex contagions in networked systems.
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Affiliation(s)
- Zixiang Yan
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Jian Gao
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yueheng Lan
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Jinghua Xiao
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
- State Key Lab of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
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14
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Ullah MS, Kamrujjaman M, Kabir KMA. Understanding the relationship between stay-at-home measures and vaccine shortages: a conventional, heterogeneous, and fractional dynamic approach. JOURNAL OF HEALTH, POPULATION, AND NUTRITION 2024; 43:32. [PMID: 38424608 DOI: 10.1186/s41043-024-00505-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/13/2024] [Indexed: 03/02/2024]
Abstract
In light of the global prevalence of a highly contagious respiratory disease, this study presents a novel approach to address the pressing and unanticipated issues by introducing a modified vaccination and lockdown-centered epidemic model. The rapid spread of the disease is attributed to viral transmissibility, the emergence of new strains (variants), lack of immunization, and human unawareness. This study aims to provide policymakers with crucial insights for making informed decisions regarding lockdown strategies, vaccine availability, and other control measures. The research adopts three types of models: deterministic, heterogeneous, and fractional-order dynamics, on both theoretical and numerical approaches. The heterogeneous network considers varying connectivity and interaction patterns among individuals, while the ABC fractional-order derivatives analyze the impact of integer-order control in different semi-groups. An extensive theoretical analysis is conducted to validate the proposed model. A comprehensive numerical investigation encompasses deterministic, stochastic, and ABC fractional-order derivatives, considering the combined effects of an effective vaccination program and non-pharmaceutical interventions, such as lockdowns and shutdowns. The findings of this research are expected to be valuable for policymakers in different countries, helping them implement dynamic strategies to control and eradicate the epidemic effectively.
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Affiliation(s)
| | | | - K M Ariful Kabir
- Department of Mathematics, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
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15
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Gu W, Li W, Gao F, Su S, Sun B, Wang W. Influence of human motion patterns on epidemic spreading dynamics. CHAOS (WOODBURY, N.Y.) 2024; 34:023101. [PMID: 38305051 DOI: 10.1063/5.0158243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024]
Abstract
Extensive real-data indicate that human motion exhibits novel patterns and has a significant impact on the epidemic spreading process. The research on the influence of human motion patterns on epidemic spreading dynamics still lacks a systematic study in network science. Based on an agent-based model, this paper simulates the spread of the disease in the gathered population by combining the susceptible-infected-susceptible epidemic process with human motion patterns, described by moving speed and gathering preference. Our simulation results show that the emergence of a hysteresis loop is observed in the system when the moving speed is slow, particularly when humans prefer to gather; that is, the epidemic prevalence of the systems depends on the fraction of initial seeds. Regardless of the gathering preference, the hysteresis loop disappears when the population moves fast. In addition, our study demonstrates that there is an optimal moving speed for the gathered population, at which the epidemic prevalence reaches its maximum value.
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Affiliation(s)
- Wenbin Gu
- School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Wenjie Li
- School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Feng Gao
- Chongqing University of Arts and Sciences, Chongqing 402160, China
| | - Sheng Su
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611713, China
| | - Baolin Sun
- School of Information Engineering, Hubei University of Economics, Wuhan 430205, China
| | - Wei Wang
- School of Public Health, Chongqing Medical University, Chongqing 400016, China
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16
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Wen J, Gabrys B, Musial K. Towards Digital Twin-Oriented Complex Networked Systems: Introducing heterogeneous node features and interaction rules. PLoS One 2024; 19:e0296426. [PMID: 38166038 PMCID: PMC10760715 DOI: 10.1371/journal.pone.0296426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/11/2023] [Indexed: 01/04/2024] Open
Abstract
This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real-world social networked systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control.
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Affiliation(s)
- Jiaqi Wen
- Complex Adaptive Systems, Data Science Institute, University of Technology Sydney, Sydney, NSW, Australia
| | - Bogdan Gabrys
- Complex Adaptive Systems, Data Science Institute, University of Technology Sydney, Sydney, NSW, Australia
| | - Katarzyna Musial
- Complex Adaptive Systems, Data Science Institute, University of Technology Sydney, Sydney, NSW, Australia
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17
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Huang Z, Shu X, Xuan Q, Ruan Z. Epidemic spreading under game-based self-quarantine behaviors: The different effects of local and global information. CHAOS (WOODBURY, N.Y.) 2024; 34:013112. [PMID: 38198677 DOI: 10.1063/5.0180484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024]
Abstract
During the outbreak of an epidemic, individuals may modify their behaviors in response to external (including local and global) infection-related information. However, the difference between local and global information in influencing the spread of diseases remains inadequately explored. Here, we study a simple epidemic model that incorporates the game-based self-quarantine behavior of individuals, taking into account the influence of local infection status, global disease prevalence, and node heterogeneity (non-identical degree distribution). Our findings reveal that local information can effectively contain an epidemic, even with only a small proportion of individuals opting for self-quarantine. On the other hand, global information can cause infection evolution curves shaking during the declining phase of an epidemic, owing to the synchronous release of nodes with the same degree from the quarantined state. In contrast, the releasing pattern under the local information appears to be more random. This shaking phenomenon can be observed in various types of networks associated with different characteristics. Moreover, it is found that under the proposed game-epidemic framework, a disease is more difficult to spread in heterogeneous networks than in homogeneous networks, which differs from conventional epidemic models.
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Affiliation(s)
- Zegang Huang
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
- Binjiang Cyberspace Security Institute of ZJUT, Hangzhou 310051, China
| | - Xincheng Shu
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
- Binjiang Cyberspace Security Institute of ZJUT, Hangzhou 310051, China
| | - Qi Xuan
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
- Binjiang Cyberspace Security Institute of ZJUT, Hangzhou 310051, China
| | - Zhongyuan Ruan
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
- Binjiang Cyberspace Security Institute of ZJUT, Hangzhou 310051, China
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18
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Tatsukawa Y, Arefin MR, Kuga K, Tanimoto J. An agent-based nested model integrating within-host and between-host mechanisms to predict an epidemic. PLoS One 2023; 18:e0295954. [PMID: 38100436 PMCID: PMC10723725 DOI: 10.1371/journal.pone.0295954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
The COVID-19 pandemic has remarkably heightened concerns regarding the prediction of communicable disease spread. This study introduces an innovative agent-based modeling approach. In this model, the quantification of human-to-human transmission aligns with the dynamic variations in the viral load within an individual, termed "within-host" and adheres to the susceptible-infected-recovered (SIR) process, referred to as "between-host." Variations in the viral load over time affect the infectivity between individual agents. This model diverges from the traditional SIR model, which employs a constant transmission probability, by incorporating a dynamic, time-dependent transmission probability influenced by the viral load in a host agent. The proposed model retains the time-integrated transmission probability characteristic of the conventional SIR model. As observed in this model, the overall epidemic size remains consistent with the predictions of the standard SIR model. Nonetheless, compared to predictions based on the classical SIR process, notable differences existed in the peak number of the infected individuals and the timing of this peak. These nontrivial differences are induced by the direct correlation between the time-evolving transmission probability and the viral load within a host agent. The developed model can inform targeted intervention strategies and public health policies by providing detailed insights into disease spread dynamics, crucial for effectively managing epidemics.
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Affiliation(s)
- Yuichi Tatsukawa
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, Japan
- MRI Research Associates Inc., Tokyo, Japan
| | - Md. Rajib Arefin
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, Japan
- Department of Mathematics, University of Dhaka, Dhaka, Bangladesh
| | - Kazuki Kuga
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, Japan
- Faculty of Engineering Sciences, Kyushu University, Fukuoka, Japan
| | - Jun Tanimoto
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, Japan
- Faculty of Engineering Sciences, Kyushu University, Fukuoka, Japan
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19
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Chen C, Tassou A, Morales V, Scherrer G. Graph theory analysis reveals an assortative pain network vulnerable to attacks. Sci Rep 2023; 13:21985. [PMID: 38082002 PMCID: PMC10713541 DOI: 10.1038/s41598-023-49458-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023] Open
Abstract
The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we employ graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Within this network, 63% of brain areas share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes that have similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain that processes nociceptive information. Furthermore, the vulnerability of this network to attack presents the possibility of alleviating pain by targeting the most connected brain areas in the network.
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Affiliation(s)
- Chong Chen
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Pharmacology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Adrien Tassou
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Pharmacology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Valentina Morales
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Pharmacology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Grégory Scherrer
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Pharmacology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- New York Stem Cell Foundation ‒ Robertson Investigator, Chapel Hill, NC, 27599, USA.
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20
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Lipowski A, Ferreira AL, Lipowska D. Heat-Bath and Metropolis Dynamics in Ising-like Models on Directed Regular Random Graphs. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1615. [PMID: 38136495 PMCID: PMC10743282 DOI: 10.3390/e25121615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/24/2023]
Abstract
Using a single-site mean-field approximation (MFA) and Monte Carlo simulations, we examine Ising-like models on directed regular random graphs. The models are directed-network implementations of the Ising model, Ising model with absorbing states, and majority voter models. When these nonequilibrium models are driven by the heat-bath dynamics, their stationary characteristics, such as magnetization, are correctly reproduced by MFA as confirmed by Monte Carlo simulations. It turns out that MFA reproduces the same result as the generating functional analysis that is expected to provide the exact description of such models. We argue that on directed regular random graphs, the neighbors of a given vertex are typically uncorrelated, and that is why MFA for models with heat-bath dynamics provides their exact description. For models with Metropolis dynamics, certain additional correlations become relevant, and MFA, which neglects these correlations, is less accurate. Models with heat-bath dynamics undergo continuous phase transition, and at the critical point, the power-law time decay of the order parameter exhibits the behavior of the Ising mean-field universality class. Analogous phase transitions for models with Metropolis dynamics are discontinuous.
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Affiliation(s)
- Adam Lipowski
- Faculty of Physics, Adam Mickiewicz University in Poznań, 61-614 Poznań, Poland
| | - António L. Ferreira
- Departamento de Física, I3N, Universidade de Aveiro, 3810-193 Aveiro, Portugal;
| | - Dorota Lipowska
- Faculty of Modern Languages and Literatures, Adam Mickiewicz University in Poznań, 61-874 Poznań, Poland;
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21
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Cheng X, Wang Y, Huang G. Edge-based compartmental modeling for the spread of cholera on random networks: A case study in Somalia. Math Biosci 2023; 366:109092. [PMID: 37923290 DOI: 10.1016/j.mbs.2023.109092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/22/2023] [Accepted: 10/22/2023] [Indexed: 11/07/2023]
Abstract
Cholera remains a major public health problem that threatens human health worldwide and its severity is continuing. In this paper, an edge-based model for cholera transmission on random networks is proposed and investigated. The model assumes that two communities share a common water source and includes three transmission routes, namely intra- and inter-community human-to-human transmission as well as water-to-human transmission. Intra-community human-to-human contacts are modeled through a random contact network, while both inter-community and water-to-human transmission are modeled through external nodes that reach each individual in the network to the same extent. The basic reproduction number and the equations of the final epidemic size are obtained. In addition, our study considers the cholera situation in Banadir, which is one of the most severely infected regions in Somalia, during the period (2019-2021). According to the geographical location, two adjacent districts are selected and our model fits well with the real data on the monthly cumulative cholera cases of these two districts during the above-mentioned period. From the perspective of network topology, cutting off high-risk contacts by supervising, isolating, quarantining and closing places with high-degree cholera-infected individuals to reduce degree heterogeneity is an effective measure to control cholera transmission. Our findings might offer some useful insights on cholera control.
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Affiliation(s)
- Xinxin Cheng
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Yi Wang
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Gang Huang
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China.
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22
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Kwon Y, Jung JH, Eom YH. Global efficiency and network structure of urban traffic flows: A percolation-based empirical analysis. CHAOS (WOODBURY, N.Y.) 2023; 33:113104. [PMID: 37909897 DOI: 10.1063/5.0150217] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 10/11/2023] [Indexed: 11/03/2023]
Abstract
Making the connection between the function and structure of networked systems is one of the fundamental issues in complex systems and network science. Urban traffic flows are related to various problems in cities and can be represented as a network of local traffic flows. To identify an empirical relation between the function and network structure of urban traffic flows, we construct a time-varying traffic flow network of a megacity, Seoul, and analyze its global efficiency with a percolation-based approach. Comparing the real-world traffic flow network with its corresponding null-model network having a randomized structure, we show that the real-world network is less efficient than its null-model network during rush hour, yet more efficient during non-rush hour. We observe that in the real-world network, links with the highest betweenness tend to have lower quality during rush hour compared to links with lower betweenness, but higher quality during non-rush hour. Since the top betweenness links tend to be the bridges that connect the network together, their congestion has a stronger impact on the network's global efficiency. Our results suggest that the spatial structure of traffic flow networks is important to understand their function.
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Affiliation(s)
- Yungi Kwon
- Natural Science Research Institute, University of Seoul, Seoul 02504, Republic of Korea
| | - Jung-Hoon Jung
- Department of Physics, University of Seoul, Seoul 02504, Republic of Korea
| | - Young-Ho Eom
- Natural Science Research Institute, University of Seoul, Seoul 02504, Republic of Korea
- Department of Physics, University of Seoul, Seoul 02504, Republic of Korea
- Urban Big Data and AI Institute, University of Seoul, Seoul 02504, Republic of Korea
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23
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Wei Z, Zhuang J. On the adoption of nonpharmaceutical interventions during the pandemic: An evolutionary game model. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:2298-2311. [PMID: 36635059 DOI: 10.1111/risa.14093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The adoption of behavioral nonpharmaceutical interventions (NPIs) among the public is essential for tackling the COVID-19 pandemic, yet presents challenges due to the complexity of human behaviors. A large body of literature has utilized classic game theory to investigate the population's decisions regarding the adoption of interventions, where the static solution concept such as the Nash equilibrium is studied. However, individual adoption behavior is not static, instead it is a dynamic process that involves the strategic interactions with other counterparts over time. The study of quantitatively analyzing the dynamics on precautionary behavior during an outbreak is rather scarce. This article fills the research gap by developing an evolutionary game-theoretic framework to model the dynamics of population behavior on the adoption of NPI. We construct the two-group asymmetric game, where behavioral change for each group is characterized by replicator equations. Sensitivity analyses are performed to examine the long-term stability of equilibrium points with respect to perturbation of model parameters. We found that the limiting behavior of intervention adoption in the population consists of only pure strategies in a game setting, indicating that the evolutionary outcome is that everyone either takes up the preventive measure or not. We also applied the framework to examine the mask-wearing behavior, and validated with actual data. Overall, this article provides insights into population dynamics on the adoption of intervention strategy during the outbreak, which can be beneficial for policy makers to better understand the evolutionary trajectory of population behavior.
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Affiliation(s)
- Zhiyuan Wei
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York, USA
| | - Jun Zhuang
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York, USA
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24
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Dou G. Scalable parallel and distributed simulation of an epidemic on a graph. PLoS One 2023; 18:e0291871. [PMID: 37773940 PMCID: PMC10540973 DOI: 10.1371/journal.pone.0291871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/07/2023] [Indexed: 10/01/2023] Open
Abstract
We propose an algorithm to simulate Markovian SIS epidemics with homogeneous rates and pairwise interactions on a fixed undirected graph, assuming a distributed memory model of parallel programming and limited bandwidth. This setup can represent a broad class of simulation tasks with compartmental models. Existing solutions for such tasks are sequential by nature. We provide an innovative solution that makes trade-offs between statistical faithfulness and parallelism possible. We offer an implementation of the algorithm in the form of pseudocode in the Appendix. Also, we analyze its algorithmic complexity and its induced dynamical system. Finally, we design experiments to show its scalability and faithfulness. In our experiments, we discover that graph structures that admit good partitioning schemes, such as the ones with clear community structures, together with the correct application of a graph partitioning method, can lead to better scalability and faithfulness. We believe this algorithm offers a way of scaling out, allowing researchers to run simulation tasks at a scale that was not accessible before. Furthermore, we believe this algorithm lays a solid foundation for extensions to more advanced epidemic simulations and graph dynamics in other fields.
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Affiliation(s)
- Guohao Dou
- School of Computer and Communication Sciences, EPFL, Lausanne, Vaud, Switzerland
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25
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Lu J, Du H, He X. A hypernetwork-based urn model for explaining collective dynamics. PLoS One 2023; 18:e0291778. [PMID: 37725633 PMCID: PMC10508602 DOI: 10.1371/journal.pone.0291778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 09/05/2023] [Indexed: 09/21/2023] Open
Abstract
The topological characterization of complex systems has significantly contributed to our understanding of the principles of collective dynamics. However, the representation of general complex networks is not enough for explaining certain problems, such as collective actions. Considering the effectiveness of hypernetworks on modeling real-world complex networks, in this paper, we proposed a hypernetwork-based Pólya urn model that considers the effect of group identity. The mathematical deduction and simulation experiments show that social influence provides a strong imitation environment for individuals, which can prevent the dynamics from being self-correcting. Additionally, the unpredictability of the social system increases with growing social influence, and the effect of group identity can moderate market inequality caused by individual preference and social influence. The present work provides a modeling basis for a better understanding of the logic of collective dynamics.
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Affiliation(s)
- Jiali Lu
- School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Haifeng Du
- School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Xiaochen He
- School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
- School of Economics and Finance, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
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26
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Silva TC, Anghinoni L, Chagas CPD, Zhao L, Tabak BM. Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6758. [PMID: 37754616 PMCID: PMC10531329 DOI: 10.3390/ijerph20186758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/20/2023] [Accepted: 09/06/2023] [Indexed: 09/28/2023]
Abstract
In this study, we investigate the COVID-19 epidemics in Brazilian cities, using early-time approximations of the SIR model in networks and combining the VAR (vector autoregressive) model with machine learning techniques. Different from other works, the underlying network was constructed by inputting real-world data on local COVID-19 cases reported by Brazilian cities into a regularized VAR model. This model estimates directional COVID-19 transmission channels (connections or links between nodes) of each pair of cities (vertices or nodes) using spectral network analysis. Despite the simple epidemiological model, our predictions align well with the real COVID-19 dynamics across Brazilian municipalities, using data only up until May 2020. Given the rising number of infectious people in Brazil-a possible indicator of a second wave-these early-time approximations could be valuable in gauging the magnitude of the next contagion peak. We further examine the effect of public health policies, including social isolation and mask usage, by creating counterfactual scenarios to quantify the human impact of these public health measures in reducing peak COVID-19 cases. We discover that the effectiveness of social isolation and mask usage varies significantly across cities. We hope our study will support the development of future public health measures.
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Affiliation(s)
- Thiago Christiano Silva
- Universidade Católica de Brasília, Brasilia 71966-700, Brazil
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences, and Literatures in Ribeirão Preto, Universidade de São Paulo, São Paulo 14040-901, Brazil
| | - Leandro Anghinoni
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences, and Literatures in Ribeirão Preto, Universidade de São Paulo, São Paulo 14040-901, Brazil
| | | | - Liang Zhao
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences, and Literatures in Ribeirão Preto, Universidade de São Paulo, São Paulo 14040-901, Brazil
| | - Benjamin Miranda Tabak
- FGV/EPPG Escola de Políticas Públicas e Governo, Fundação Getúlio Vargas (School of Public Policy and Government, Getulio Vargas Foundation), Brasilia 70830-020, Brazil
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27
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Policarpo JMP, Ramos AAGF, Dye C, Faria NR, Leal FE, Moraes OJS, Parag KV, Peixoto PS, Buss L, Sabino EC, Nascimento VH, Deppman A. Scale-free dynamics of COVID-19 in a Brazilian city. APPLIED MATHEMATICAL MODELLING 2023; 121:166-184. [PMID: 37151217 PMCID: PMC10154131 DOI: 10.1016/j.apm.2023.03.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 03/13/2023] [Accepted: 03/29/2023] [Indexed: 05/09/2023]
Abstract
A common basis to address the dynamics of directly transmitted infectious diseases, such as COVID-19, are compartmental (or SIR) models. SIR models typically assume homogenous population mixing, a simplification that is convenient but unrealistic. Here we validate an existing model of a scale-free fractal infection process using high-resolution data on COVID-19 spread in São Caetano, Brazil. We find that transmission can be described by a network in which each infectious individual has a small number of susceptible contacts, of the order of 2-5. This model parameter correlated tightly with physical distancing measured by mobile phone data, such that in periods of greater distancing the model recovered a lower average number of contacts, and vice versa. We show that the SIR model is a special case of our scale-free fractal process model in which the parameter that reflects population structure is set at unity, indicating homogeneous mixing. Our more general framework better explained the dynamics of COVID-19 in São Caetano, used fewer parameters than a standard SIR model and accounted for geographically localized clusters of disease. Our model requires further validation in other locations and with other directly transmitted infectious agents.
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Affiliation(s)
| | - A A G F Ramos
- Instituto de Física - Universidade de São Paulo, Brazil
| | - C Dye
- Department of Biology, University of Oxford, UK
| | - N R Faria
- Department of Biology, University of Oxford, UK
- Imperial Coll London, MRC Ctr Global Infect Dis Anal, Sch Publ Helth, London, England, UK
- Faculdade de Medicina - Universidade de São Paulo, Brazil
| | - F E Leal
- Universidade de São Caetano do Sul, São Caetano do Sul and Programa de Oncovirologia - Instituto Nacional de Câncer, Rio de Janeiro, Brazil
| | - O J S Moraes
- Instituto de Física - Universidade de São Paulo, Brazil
| | - K V Parag
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London W2 1PG, UK
| | - P S Peixoto
- Instituto de Matemática e Estatística, Universidade de São Paulo, Brazil
| | - L Buss
- Faculdade de Medicina - Universidade de São Paulo, Brazil
| | - E C Sabino
- Faculdade de Medicina - Universidade de São Paulo, Brazil
| | | | - A Deppman
- Instituto de Física - Universidade de São Paulo, Brazil
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28
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Peng W, Chen T, Zheng B, Jiang X. Spreading Dynamics of Capital Flow Transfer in Complex Financial Networks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1240. [PMID: 37628270 PMCID: PMC10452986 DOI: 10.3390/e25081240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/09/2023] [Accepted: 08/20/2023] [Indexed: 08/27/2023]
Abstract
The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies.
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Affiliation(s)
- Wenyan Peng
- Department of Physics, Zhejiang University, Hangzhou 310018, China;
| | - Tingting Chen
- Department of Finance, Zhejiang University of Finance and Economics, Hangzhou 310018, China
| | - Bo Zheng
- Department of Physics, Zhejiang University, Hangzhou 310018, China;
- School of Physics and Astronomy, Yunnan University, Kunming 650091, China
| | - Xiongfei Jiang
- College of Finance and Information, Ningbo University of Finance and Economics, Ningbo 315175, China;
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29
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Lima LL, Atman APF. Complexity in the dengue spreading: A network analysis approach. PLoS One 2023; 18:e0289690. [PMID: 37549129 PMCID: PMC10406222 DOI: 10.1371/journal.pone.0289690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/24/2023] [Indexed: 08/09/2023] Open
Abstract
In an increasingly interconnected society, preventing epidemics has become a major challenge. Numerous infectious diseases spread between individuals by a vector, creating bipartite networks of infection with the characteristics of complex networks. In the case of dengue, a mosquito-borne disease, these infection networks include a vector-the Aedes aegypti mosquito-which has expanded its endemic area due to climate change. In this scenario, innovative approaches are essential to help public agents in the fight against the disease. Using an agent-based model, we investigated the network morphology of a dengue endemic region considering four different serotypes and a small population. The degree, betweenness, and closeness distributions are evaluated for the bipartite networks, considering the interactions up to the second order for each serotype. We observed scale-free features and heavy tails in the degree distribution and betweenness and quantified the decay of the degree distribution with a q-Gaussian fit function. The simulation results indicate that the spread of dengue is primarily driven by human-to-human and human-to-mosquito interaction, reinforcing the importance of controlling the vector to prevent episodes of epidemic outbreaks.
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Affiliation(s)
- L. L. Lima
- Programa de Pos-Graduação em Modelagem Matemática e Computacional, Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - A. P. F. Atman
- Programa de Pos-Graduação em Modelagem Matemática e Computacional, Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Departamento de Física, Centro Federal de Educação Tecnológica de Minas Gerais- CEFET-MG, Belo Horizonte, Minas Gerais, Brazil
- National Institute of Science and Technology for Complex Systems-CEFET-MG, Belo Horizonte, Minas Gerais, Brazil
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30
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Kates-Harbeck J, Desai MM. Social network structure and the spread of complex contagions from a population genetics perspective. Phys Rev E 2023; 108:024306. [PMID: 37723694 DOI: 10.1103/physreve.108.024306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 06/30/2023] [Indexed: 09/20/2023]
Abstract
Ideas, behaviors, and opinions spread through social networks. If the probability of spreading to a new individual is a nonlinear function of the fraction of the individuals' affected neighbors, such a spreading process becomes a "complex contagion." This nonlinearity does not typically appear with physically spreading infections, but instead can emerge when the concept that is spreading is subject to game theoretical considerations (e.g., for choices of strategy or behavior) or psychological effects such as social reinforcement and other forms of peer influence (e.g., for ideas, preferences, or opinions). Here we study how the stochastic dynamics of such complex contagions are affected by the underlying network structure. Motivated by simulations of complex contagions on real social networks, we present a framework for analyzing the statistics of contagions with arbitrary nonlinear adoption probabilities based on the mathematical tools of population genetics. The central idea is to use an effective lower-dimensional diffusion process to approximate the statistics of the contagion. This leads to a tradeoff between the effects of "selection" (microscopic tendencies for an idea to spread or die out), random drift, and network structure. Our framework illustrates intuitively several key properties of complex contagions: stronger community structure and network sparsity can significantly enhance the spread, while broad degree distributions dampen the effect of selection compared to random drift. Finally, we show that some structural features can exhibit critical values that demarcate regimes where global contagions become possible for networks of arbitrary size. Our results draw parallels between the competition of genes in a population and memes in a world of minds and ideas. Our tools provide insight into the spread of information, behaviors, and ideas via social influence, and highlight the role of macroscopic network structure in determining their fate.
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Affiliation(s)
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA
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31
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Sheng A, Li A, Wang L. Evolutionary dynamics on sequential temporal networks. PLoS Comput Biol 2023; 19:e1011333. [PMID: 37549167 PMCID: PMC10434888 DOI: 10.1371/journal.pcbi.1011333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 08/17/2023] [Accepted: 07/06/2023] [Indexed: 08/09/2023] Open
Abstract
Population structure is a well-known catalyst for the evolution of cooperation and has traditionally been considered to be static in the course of evolution. Conversely, real-world populations, such as microbiome communities and online social networks, frequently show a progression from tiny, active groups to huge, stable communities, which is insufficient to be captured by constant structures. Here, we propose sequential temporal networks to characterize growing networked populations, and we extend the theory of evolutionary games to these temporal networks with arbitrary structures and growth rules. We derive analytical rules under which a sequential temporal network has a higher fixation probability for cooperation than its static counterpart. Under neutral drift, the rule is simply a function of the increment of nodes and edges in each time step. But if the selection is weak, the rule is related to coalescence times on networks. In this case, we propose a mean-field approximation to calculate fixation probabilities and critical benefit-to-cost ratios with lower calculation complexity. Numerical simulations in empirical datasets also prove the cooperation-promoting effect of population growth. Our research stresses the significance of population growth in the real world and provides a high-accuracy approximation approach for analyzing the evolution in real-life systems.
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Affiliation(s)
- Anzhi Sheng
- Center for Systems and Control, College of Engineering, Peking University, Beijing, China
- Department of Biology, University of Pennsylvania, Philadelphia, United States of America
| | - Aming Li
- Center for Systems and Control, College of Engineering, Peking University, Beijing, China
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing, China
| | - Long Wang
- Center for Systems and Control, College of Engineering, Peking University, Beijing, China
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing, China
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32
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Merbis W, de Mulatier C, Corboz P. Efficient simulations of epidemic models with tensor networks: Application to the one-dimensional susceptible-infected-susceptible model. Phys Rev E 2023; 108:024303. [PMID: 37723790 DOI: 10.1103/physreve.108.024303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/20/2023] [Indexed: 09/20/2023]
Abstract
The contact process is an emblematic model of a nonequilibrium system, containing a phase transition between inactive and active dynamical regimes. In the epidemiological context, the model is known as the susceptible-infected-susceptible model, and it is widely used to describe contagious spreading. In this work, we demonstrate how accurate and efficient representations of the full probability distribution over all configurations of the contact process on a one-dimensional chain can be obtained by means of matrix product states (MPSs). We modify and adapt MPS methods from many-body quantum systems to study the classical distributions of the driven contact process at late times. We give accurate and efficient results for the distribution of large gaps, and illustrate the advantage of our methods over Monte Carlo simulations. Furthermore, we study the large deviation statistics of the dynamical activity, defined as the total number of configuration changes along a trajectory, and investigate quantum-inspired entropic measures, based on the second Rényi entropy.
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Affiliation(s)
- Wout Merbis
- Dutch Institute for Emergent Phenomena (DIEP) & Institute for Theoretical Physics (ITFA), University of Amsterdam, 1090 GL Amsterdam, The Netherlands
| | - Clélia de Mulatier
- Dutch Institute for Emergent Phenomena (DIEP) & Institute for Theoretical Physics (ITFA), University of Amsterdam, 1090 GL Amsterdam, The Netherlands
| | - Philippe Corboz
- Dutch Institute for Emergent Phenomena (DIEP) & Institute for Theoretical Physics (ITFA), University of Amsterdam, 1090 GL Amsterdam, The Netherlands
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Sterchi M, Hilfiker L, Grütter R, Bernstein A. Active querying approach to epidemic source detection on contact networks. Sci Rep 2023; 13:11363. [PMID: 37443324 PMCID: PMC10345105 DOI: 10.1038/s41598-023-38282-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/06/2023] [Indexed: 07/15/2023] Open
Abstract
The problem of identifying the source of an epidemic (also called patient zero) given a network of contacts and a set of infected individuals has attracted interest from a broad range of research communities. The successful and timely identification of the source can prevent a lot of harm as the number of possible infection routes can be narrowed down and potentially infected individuals can be isolated. Previous research on this topic often assumes that it is possible to observe the state of a substantial fraction of individuals in the network before attempting to identify the source. We, on the contrary, assume that observing the state of individuals in the network is costly or difficult and, hence, only the state of one or few individuals is initially observed. Moreover, we presume that not only the source is unknown, but also the duration for which the epidemic has evolved. From this more general problem setting a need to query the state of other (so far unobserved) individuals arises. In analogy with active learning, this leads us to formulate the active querying problem. In the active querying problem, we alternate between a source inference step and a querying step. For the source inference step, we rely on existing work but take a Bayesian perspective by putting a prior on the duration of the epidemic. In the querying step, we aim to query the states of individuals that provide the most information about the source of the epidemic, and to this end, we propose strategies inspired by the active learning literature. Our results are strongly in favor of a querying strategy that selects individuals for whom the disagreement between individual predictions, made by all possible sources separately, and a consensus prediction is maximal. Our approach is flexible and, in particular, can be applied to static as well as temporal networks. To demonstrate our approach's practical importance, we experiment with three empirical (temporal) contact networks: a network of pig movements, a network of sexual contacts, and a network of face-to-face contacts between residents of a village in Malawi. The results show that active querying strategies can lead to substantially improved source inference results as compared to baseline heuristics. In fact, querying only a small fraction of nodes in a network is often enough to achieve a source inference performance comparable to a situation where the infection states of all nodes are known.
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Affiliation(s)
- Martin Sterchi
- Department of Informatics, University of Zurich, 8050, Zurich, Switzerland.
- School of Business, University of Applied Sciences and Arts FHNW, 4600, Olten, Switzerland.
- Swiss Federal Research Institute WSL, 8903, Birmensdorf, Switzerland.
| | - Lorenz Hilfiker
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, 3012, Bern, Switzerland
| | - Rolf Grütter
- Swiss Federal Research Institute WSL, 8903, Birmensdorf, Switzerland
| | - Abraham Bernstein
- Department of Informatics, University of Zurich, 8050, Zurich, Switzerland
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Yao HG, Zhang H. Critical and steady-state characteristics of delay propagation in an airport network. PLoS One 2023; 18:e0288200. [PMID: 37418454 DOI: 10.1371/journal.pone.0288200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Accepted: 06/21/2023] [Indexed: 07/09/2023] Open
Abstract
In this work, we established a density equation for delayed airports to investigate the horizontal propagation mechanism of delays among airports in an airport network. We explored the critical conditions, steady-state features, and scale of the delay propagation, and designed a simulation system to verify the accuracy of the results. The results indicated that, due to the no-table scale-free feature of an airport network, the critical value of delay propagation is extremely small, and delays are prone to propagate among airports. Furthermore, as delay propagation reaches a steady state in an aviation network, the degree value of the node becomes highly correlated with its delay state. Hub airports with high degree values are the most prone to being affected by delay propagation. In addition, the number of airports that are initially delayed influences the time required for delay propagation to reach a steady state. Specifically, if there are fewer initially delayed airports, a longer time is required to reach a steady state. In the steady state, the delay ratios of airports with different degree values in the network converge to a balance point. The delay degree of the node is highly positively correlated with the delay propagation rate in the network, but negatively related to the degree distribution index of the network.
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Affiliation(s)
- Hong-Guang Yao
- College of Aviation Transportation, Shanghai University of Engineering Science, Shanghai, China
| | - Hang Zhang
- College of Aviation Transportation, Shanghai University of Engineering Science, Shanghai, China
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35
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Merbis W, de Domenico M. Emergent information dynamics in many-body interconnected systems. Phys Rev E 2023; 108:014312. [PMID: 37583168 DOI: 10.1103/physreve.108.014312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 07/10/2023] [Indexed: 08/17/2023]
Abstract
The information implicitly represented in the state of physical systems allows for their analysis using analytical techniques from statistical mechanics and information theory. This approach has been successfully applied to complex networks, including biophysical systems such as virus-host protein-protein interactions and whole-brain models in health and disease, drawing inspiration from quantum statistical physics. Here we propose a general mathematical framework for modeling information dynamics on complex networks, where the internal node states are vector valued, allowing each node to carry multiple types of information. This setup is relevant for various biophysical and sociotechnological models of complex systems, ranging from viral dynamics on networks to models of opinion dynamics and social contagion. Instead of focusing on node-node interactions, we shift our attention to the flow of information between network configurations. We uncover fundamental differences between widely used spin models on networks, such as voter and kinetic dynamics, which cannot be detected through classical node-based analysis. We illustrate the mathematical framework further through an exemplary application to epidemic spreading on a low-dimensional network. Our model provides an opportunity to adapt powerful analytical methods from quantum many-body systems to study the interplay between structure and dynamics in interconnected systems.
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Affiliation(s)
- Wout Merbis
- Dutch Institute for Emergent Phenomena (DIEP), Institute for Theoretical Physics (ITFA), University of Amsterdam, 1090 GL Amsterdam, The Netherlands
| | - Manlio de Domenico
- Department of Physics and Astronomy "Galileo Galilei," University of Padua, Via F. Marzolo 8, 315126 Padua, Italy and Istituto Nazionale di Fisica Nucleare, Sez. Padua, Italy
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36
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Zhang H, Cao L, Fu C, Cai S, Gao Y. Epidemic spreading on multi-layer networks with active nodes. CHAOS (WOODBURY, N.Y.) 2023; 33:073128. [PMID: 37459223 DOI: 10.1063/5.0151777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 06/09/2023] [Indexed: 07/20/2023]
Abstract
Investigations on spreading dynamics based on complex networks have received widespread attention these years due to the COVID-19 epidemic, which are conducive to corresponding prevention policies. As for the COVID-19 epidemic itself, the latent time and mobile crowds are two important and inescapable factors that contribute to the significant prevalence. Focusing on these two factors, this paper systematically investigates the epidemic spreading in multiple spaces with mobile crowds. Specifically, we propose a SEIS (Susceptible-Exposed-Infected-Susceptible) model that considers the latent time based on a multi-layer network with active nodes which indicate the mobile crowds. The steady-state equations and epidemic threshold of the SEIS model are deduced and discussed. And by comprehensively discussing the key model parameters, we find that (1) due to the latent time, there is a "cumulative effect" on the infected, leading to the "peaks" or "shoulders" of the curves of the infected individuals, and the system can switch among three states with the relative parameter combinations changing; (2) the minimal mobile crowds can also cause the significant prevalence of the epidemic at the steady state, which is suggested by the zero-point phase change in the proportional curves of infected individuals. These results can provide a theoretical basis for formulating epidemic prevention policies.
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Affiliation(s)
- Hu Zhang
- School of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China
- Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Lingling Cao
- School of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Chuanji Fu
- School of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shimin Cai
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yachun Gao
- School of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China
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37
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Xiao R, Gao Q, Azaele S, Sun Y. Effects of noise on the critical points of Turing instability in complex ecosystems. Phys Rev E 2023; 108:014407. [PMID: 37583214 DOI: 10.1103/physreve.108.014407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/01/2023] [Indexed: 08/17/2023]
Abstract
Noise is ubiquitous in natural and artificial systems. In a noisy environment, the interactions among nodes may fluctuate randomly, leading to more complicated interactions. In this paper we focus on the effects of noise and network topology on the Turing pattern of ecological networks with activator-inhibitor structure, which may be interpreted as prey-predator interactions. Based on the stability theory of stochastic differential equations, a sufficient condition for the uniform state is derived. The analytical results indicate that noise is beneficial for the uniform state. When the ratio between the diffusion coefficients of the predator and prey increases, the ecosystems can exhibit a transition from a uniform stable state to a Turing pattern, while when the ratio decreases, the ecosystems transit from a Turing pattern to a uniform stable state. There are two crucial critical points in Turing patterns, forward and backward. We find that both forward and backward critical points increase as the noise intensity increases. This means that noise favors a stable homogeneous state compared to a state with a heterogeneous pattern, which is consistent with the analytical results. In addition, noise can weaken the hysteresis phenomenon and even eliminate it in some cases. Furthermore, we report that network topology plays an important role in modulating the uniform state of ecosystems, such as the size of prey-predator systems, the network connectivity, and the strength of interaction.
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Affiliation(s)
- Rui Xiao
- School of Mathematics, China University of Mining and Technology, Xuzhou 221116, China
| | - Qingyu Gao
- College of Chemical Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Sandro Azaele
- Department of Physics and Astronomy "G. Galileo," University of Padova, Padova Via Francesco Marzolo 8, 35131 Padova, Italy
| | - Yongzheng Sun
- School of Mathematics, China University of Mining and Technology, Xuzhou 221116, China
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38
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Alencar DSM, Alves TFA, Lima FWS, Ferreira RS, Alves GA, Macedo-Filho A. Droplet finite-size scaling of the majority-vote model on scale-free networks. Phys Rev E 2023; 108:014308. [PMID: 37583232 DOI: 10.1103/physreve.108.014308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/07/2023] [Indexed: 08/17/2023]
Abstract
We discuss the majority vote model coupled with scale-free networks and investigate its critical behavior. Previous studies point to a nonuniversal behavior of the majority vote model, where the critical exponents depend on the connectivity. At the same time, the effective dimension D_{eff} is unity for a degree distribution exponent 5/2<γ<7/2. We introduce a finite-size theory of the majority vote model for uncorrelated networks and present generalized scaling relations with good agreement with Monte Carlo simulation results. Our finite-size approach has two sources of size dependence: an external field representing the influence of the mass media on consensus formation and the scale-free network cutoff. The critical exponents are nonuniversal, dependent on the degree distribution exponent, precisely when 5/2<γ<7/2. For γ≥7/2, the model is in the same universality class as the majority vote model on Erdős-Rényi random graphs. However, for γ=7/2, the critical behavior includes additional logarithmic corrections.
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Affiliation(s)
- D S M Alencar
- Departamento de Física, Universidade Federal do Piauí, 57072-970 Teresina - PI, Brazil
| | - T F A Alves
- Departamento de Física, Universidade Federal do Piauí, 57072-970 Teresina - PI, Brazil
| | - F W S Lima
- Departamento de Física, Universidade Federal do Piauí, 57072-970 Teresina - PI, Brazil
| | - R S Ferreira
- Departamento de Ciências Exatas e Aplicadas, Universidade Federal de Ouro Preto, 35931-008 João Monlevade - MG, Brazil
| | - G A Alves
- Departamento de Física, Universidade Estadual do Piauí, 64002-150 Teresina - PI, Brazil
| | - A Macedo-Filho
- Departamento de Física, Universidade Estadual do Piauí, 64002-150 Teresina - PI, Brazil
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39
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Sulaimon TA, Chaters GL, Nyasebwa OM, Swai ES, Cleaveland S, Enright J, Kao RR, Johnson PCD. Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information. Front Vet Sci 2023; 10:1049633. [PMID: 37456963 PMCID: PMC10340087 DOI: 10.3389/fvets.2023.1049633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Livestock movements contribute to the spread of several infectious diseases. Data on livestock movements can therefore be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases, including Rift Valley fever (RVF)-a vaccine-preventable arboviral fever. Detailed livestock movement data are known to be useful for targeting control efforts including vaccination. These data are available in many countries, however, such data are generally lacking in others, including many in East Africa, where multiple RVF outbreaks have been reported in recent years. Available movement data are imperfect, and the impact of this uncertainty in the utility of movement data on informing targeting of vaccination is not fully understood. Here, we used a network simulation model to describe the spread of RVF within and between 398 wards in northern Tanzania connected by cattle movements, on which we evaluated the impact of targeting vaccination using imperfect movement data. We show that pre-emptive vaccination guided by only market movement permit data could prevent large outbreaks. Targeted control (either by the risk of RVF introduction or onward transmission) at any level of imperfect movement information is preferred over random vaccination, and any improvement in information reliability is advantageous to their effectiveness. Our modeling approach demonstrates how targeted interventions can be effectively used to inform animal and public health policies for disease control planning. This is particularly valuable in settings where detailed data on livestock movements are either unavailable or imperfect due to resource limitations in data collection, as well as challenges associated with poor compliance.
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Affiliation(s)
- Tijani A. Sulaimon
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, United Kingdom
- Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, United Kingdom
| | - Gemma L. Chaters
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
- Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
- Global Burden of Animal Diseases (GBADs) Programme, University of Liverpool, Liverpool, United Kingdom
| | - Obed M. Nyasebwa
- Veterinary Council of Tanzania, Ministry of Livestock and Fisheries, Dodoma, Tanzania
| | - Emanuel S. Swai
- Department of Veterinary Services, Ministry of Livestock and Fisheries, Dodoma, Tanzania
| | - Sarah Cleaveland
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Jessica Enright
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Rowland R. Kao
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, United Kingdom
- Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, United Kingdom
- School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom
| | - Paul C. D. Johnson
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
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40
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Brabers JHVJ. The spread of infectious diseases from a physics perspective. Biol Methods Protoc 2023; 8:bpad010. [PMID: 37662617 PMCID: PMC10469146 DOI: 10.1093/biomethods/bpad010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 09/05/2023] Open
Abstract
This article deals with the spread of infectious diseases from a physics perspective. It considers a population as a network of nodes representing the population members, linked by network edges representing the (social) contacts of the individual population members. Infections spread along these edges from one node (member) to another. This article presents a novel, modified version of the SIR compartmental model, able to account for typical network effects and percolation phenomena. The model is successfully tested against the results of simulations based on Monte-Carlo methods. Expressions for the (basic) reproduction numbers in terms of the model parameters are presented, and justify some mild criticisms on the widely spread interpretation of reproduction numbers as being the number of secondary infections due to a single active infection. Throughout the article, special emphasis is laid on understanding, and on the interpretation of phenomena in terms of concepts borrowed from condensed-matter and statistical physics, which reveals some interesting analogies. Percolation effects are of particular interest in this respect and they are the subject of a detailed investigation. The concept of herd immunity (its definition and nature) is intensively dealt with as well, also in the context of large-scale vaccination campaigns and waning immunity. This article elucidates how the onset of herd-immunity can be considered as a second-order phase transition in which percolation effects play a crucial role, thus corroborating, in a more pictorial/intuitive way, earlier viewpoints on this matter. An exact criterium for the most relevant form of herd-immunity to occur can be derived in terms of the model parameters. The analyses presented in this article provide insight in how various measures to prevent an epidemic spread of an infection work, how they can be optimized and what potentially deceptive issues have to be considered when such measures are either implemented or scaled down.
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Cencetti G, Contreras DA, Mancastroppa M, Barrat A. Distinguishing Simple and Complex Contagion Processes on Networks. PHYSICAL REVIEW LETTERS 2023; 130:247401. [PMID: 37390429 DOI: 10.1103/physrevlett.130.247401] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/25/2023] [Accepted: 05/17/2023] [Indexed: 07/02/2023]
Abstract
Contagion processes on networks, including disease spreading, information diffusion, or social behaviors propagation, can be modeled as simple contagion, i.e., as a contagion process involving one connection at a time, or as complex contagion, in which multiple interactions are needed for a contagion event. Empirical data on spreading processes, however, even when available, do not easily allow us to uncover which of these underlying contagion mechanisms is at work. We propose a strategy to discriminate between these mechanisms upon the observation of a single instance of a spreading process. The strategy is based on the observation of the order in which network nodes are infected, and on its correlations with their local topology: these correlations differ between processes of simple contagion, processes involving threshold mechanisms, and processes driven by group interactions (i.e., by "higher-order" mechanisms). Our results improve our understanding of contagion processes and provide a method using only limited information to distinguish between several possible contagion mechanisms.
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Affiliation(s)
| | - Diego Andrés Contreras
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
| | - Marco Mancastroppa
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
| | - Alain Barrat
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
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42
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Zhang H, Mi Y, Fu Y, Liu X, Zhang Y, Wang J, Tan J. Security defense decision method based on potential differential game for complex networks. Comput Secur 2023. [DOI: 10.1016/j.cose.2023.103187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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43
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Liu L, Jones BF, Uzzi B, Wang D. Data, measurement and empirical methods in the science of science. Nat Hum Behav 2023:10.1038/s41562-023-01562-4. [PMID: 37264084 DOI: 10.1038/s41562-023-01562-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 02/17/2023] [Indexed: 06/03/2023]
Abstract
The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding 'science of science'. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field's diverse methodologies and expand researchers' toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.
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Affiliation(s)
- Lu Liu
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA
| | - Benjamin F Jones
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- National Bureau of Economic Research, Cambridge, MA, USA
- Brookings Institution, Washington, DC, USA
| | - Brian Uzzi
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
| | - Dashun Wang
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA.
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA.
- Kellogg School of Management, Northwestern University, Evanston, IL, USA.
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
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44
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Wang G, Alias SB, Sun Z, Wang F, Fan A, Hu H. Influential nodes identification method based on adaptive adjustment of voting ability. Heliyon 2023; 9:e16112. [PMID: 37215850 PMCID: PMC10196995 DOI: 10.1016/j.heliyon.2023.e16112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 04/30/2023] [Accepted: 05/05/2023] [Indexed: 05/24/2023] Open
Abstract
Influential nodes identification technology is one of the important topics which has been widely applied to logistics node location, social information dissemination, transportation network carrying, biological virus dissemination, power network anti-destruction, etc. At present, a large number of influential nodes identification methods have been studied, but the algorithms that are simple to execute, have high accuracy and can be better applied to real networks are still the focus of research. Therefore, due to the advantages of simple to execute in voting mechanism, a novel algorithm based on adaptive adjustment of voting ability (AAVA) to identify the influential nodes is presented by considering the local attributes of node and the voting contribution of its neighbor nodes, to solve the problem of low accuracy and discrimination of the existing algorithms. This proposed algorithm uses the similarity between the voting node and the voted node to dynamically adjust its voting ability without setting any parameters, so that a node can contribute different voting abilities to different neighbor nodes. To verify the performance of AAVA algorithm, the running results of 13 algorithms are analyzed and compared on 10 different networks with the SIR model as a reference. The experimental results show that the influential nodes identified by AAVA have high consistency with SIR model in Top-10 nodes and Kendall correlation, and have better infection effect of the network. Therefore, it is proved that AAV algorithm has high accuracy and effectiveness, and can be applied to real complex networks of different types and sizes.
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Affiliation(s)
- Guan Wang
- School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China
- Faculty of Engineering, Built Environment & Information Technology, SEGI University, Malaysia
| | - Syazwina Binti Alias
- Faculty of Engineering, Built Environment & Information Technology, SEGI University, Malaysia
| | - Zejun Sun
- School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China
| | - Feifei Wang
- School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China
| | - Aiwan Fan
- School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China
| | - Haifeng Hu
- School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China
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45
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Ghosh S, Khanra P, Kundu P, Ji P, Ghosh D, Hens C. Dimension reduction in higher-order contagious phenomena. CHAOS (WOODBURY, N.Y.) 2023; 33:2893033. [PMID: 37229635 DOI: 10.1063/5.0152959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/01/2023] [Indexed: 05/27/2023]
Abstract
We investigate epidemic spreading in a deterministic susceptible-infected-susceptible model on uncorrelated heterogeneous networks with higher-order interactions. We provide a recipe for the construction of one-dimensional reduced model (resilience function) of the N-dimensional susceptible-infected-susceptible dynamics in the presence of higher-order interactions. Utilizing this reduction process, we are able to capture the microscopic and macroscopic behavior of infectious networks. We find that the microscopic state of nodes (fraction of stable healthy individual of each node) inversely scales with their degree, and it becomes diminished due to the presence of higher-order interactions. In this case, we analytically obtain that the macroscopic state of the system (fraction of infectious or healthy population) undergoes abrupt transition. Additionally, we quantify the network's resilience, i.e., how the topological changes affect the stable infected population. Finally, we provide an alternative framework of dimension reduction based on the spectral analysis of the network, which can identify the critical onset of the disease in the presence or absence of higher-order interactions. Both reduction methods can be extended for a large class of dynamical models.
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Affiliation(s)
- Subrata Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India
| | - Pitambar Khanra
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York 14260, USA
| | - Prosenjit Kundu
- Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat 382007, India
| | - Peng Ji
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
| | - Dibakar Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India
| | - Chittaranjan Hens
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India
- International Institute of Information Technology, Hyderabad 500 032, India
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46
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Schieber TA, Carpi LC, Pardalos PM, Masoller C, Díaz-Guilera A, Ravetti MG. Diffusion capacity of single and interconnected networks. Nat Commun 2023; 14:2217. [PMID: 37072418 PMCID: PMC10113202 DOI: 10.1038/s41467-023-37323-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 03/10/2023] [Indexed: 04/20/2023] Open
Abstract
Understanding diffusive processes in networks is a significant challenge in complexity science. Networks possess a diffusive potential that depends on their topological configuration, but diffusion also relies on the process and initial conditions. This article presents Diffusion Capacity, a concept that measures a node's potential to diffuse information based on a distance distribution that considers both geodesic and weighted shortest paths and dynamical features of the diffusion process. Diffusion Capacity thoroughly describes the role of individual nodes during a diffusion process and can identify structural modifications that may improve diffusion mechanisms. The article defines Diffusion Capacity for interconnected networks and introduces Relative Gain, which compares the performance of a node in a single structure versus an interconnected one. The method applies to a global climate network constructed from surface air temperature data, revealing a significant change in diffusion capacity around the year 2000, suggesting a loss of the planet's diffusion capacity that could contribute to the emergence of more frequent climatic events.
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Affiliation(s)
- Tiago A Schieber
- Departamento de Ciências Administrativas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Laura C Carpi
- Instituto Nacional de Ciência e Tecnologia, Sistemas Complexos, INCT-SC, CEFET-MG, Belo Horizonte, MG, Brazil
- Machine Intelligence and Data Science Laboratory (MINDS), Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Panos M Pardalos
- Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA
- Lab LATNA, National Research University, Higher School of Economics, Nizhny Novgorod, Russia
| | - Cristina Masoller
- Departament de Física, Universitat Politècnica de Catalunya, Terrassa, BCN, Spain
| | - Albert Díaz-Guilera
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, BCN, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona, BCN, Spain
| | - Martín G Ravetti
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
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47
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Meng X, Lin J, Fan Y, Gao F, Fenoaltea EM, Cai Z, Si S. Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic. CHAOS, SOLITONS, AND FRACTALS 2023; 169:113294. [PMID: 36891356 PMCID: PMC9977628 DOI: 10.1016/j.chaos.2023.113294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/20/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.
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Affiliation(s)
- Xueyu Meng
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
- Department of Physics, University of Fribourg, Fribourg 1700, Switzerland
| | - Jianhong Lin
- Department of Physics, University of Fribourg, Fribourg 1700, Switzerland
- Department of Management, Technology and Economics, ETH Zürich, Scheuchzerstrasse 7, CH-8092 Zürich, Switzerland
| | - Yufei Fan
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
| | - Fujuan Gao
- Department of Physics, University of Fribourg, Fribourg 1700, Switzerland
| | | | - Zhiqiang Cai
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shubin Si
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
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48
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Gao Z, Ghosh D, Harrington HA, Restrepo JG, Taylor D. Dynamics on networks with higher-order interactions. CHAOS (WOODBURY, N.Y.) 2023; 33:040401. [PMID: 37097941 DOI: 10.1063/5.0151265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Affiliation(s)
- Z Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - D Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - H A Harrington
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - J G Restrepo
- Department of Applied Mathematics, University of Colorado at Boulder, Boulder, Colorado 80309, USA
| | - D Taylor
- Department of Mathematics, University at Buffalo, State University of New York, Buffalo, New York 14260, USA
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49
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Both C, Dehmamy N, Yu R, Barabási AL. Accelerating network layouts using graph neural networks. Nat Commun 2023; 14:1560. [PMID: 36944640 PMCID: PMC10030870 DOI: 10.1038/s41467-023-37189-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 03/03/2023] [Indexed: 03/23/2023] Open
Abstract
Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret "hairball" layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm's ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.
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Affiliation(s)
- Csaba Both
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Nima Dehmamy
- MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, USA
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, CA, USA
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Data and Network Science, Central European University, Budapest, Hungary.
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50
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Chen C, Tassou A, Morales V, Scherrer G. Graph theory analysis reveals an assortative pain network vulnerable to attacks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.08.531580. [PMID: 36945626 PMCID: PMC10028857 DOI: 10.1101/2023.03.08.531580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we use graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Sixty-three percent of brain areas in this structural pain network share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes with similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain processing nociceptive information, and the vulnerability of this network to attack opens up the possibility of alleviating pain by targeting the most connected brain areas in the network.
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