1
|
Gao S, Liu Y, Wu B. The speed of neutral evolution on graphs. J R Soc Interface 2024; 21:20230594. [PMID: 38835245 DOI: 10.1098/rsif.2023.0594] [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: 10/12/2023] [Accepted: 04/02/2024] [Indexed: 06/06/2024] Open
Abstract
The speed of evolution on structured populations is crucial for biological and social systems. The likelihood of invasion is key for evolutionary stability. But it makes little sense if it takes long. It is far from known what population structure slows down evolution. We investigate the absorption time of a single neutral mutant for all the 112 non-isomorphic undirected graphs of size 6. We find that about three-quarters of the graphs have an absorption time close to that of the complete graph, less than one-third are accelerators, and more than two-thirds are decelerators. Surprisingly, determining whether a graph has a long absorption time is too complicated to be captured by the joint degree distribution. Via the largest sojourn time, we find that echo-chamber-like graphs, which consist of two homogeneous graphs connected by few sparse links, are likely to slow down absorption. These results are robust for large graphs, mutation patterns as well as evolutionary processes. This work serves as a benchmark for timing evolution with complex interactions, and fosters the understanding of polarization in opinion formation.
Collapse
Affiliation(s)
- Shun Gao
- School of Sciences, Beijing University of Posts and Telecommunications, Beijing, People's Republic of China
| | - Yuan Liu
- School of Sciences, Beijing University of Posts and Telecommunications, Beijing, People's Republic of China
| | - Bin Wu
- School of Sciences, Beijing University of Posts and Telecommunications, Beijing, People's Republic of China
| |
Collapse
|
2
|
Qiu F, Yu C, Feng Y, Li Y. Key node identification for a network topology using hierarchical comprehensive importance coefficients. Sci Rep 2024; 14:12039. [PMID: 38802476 PMCID: PMC11130280 DOI: 10.1038/s41598-024-62895-2] [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: 02/17/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024] Open
Abstract
Key nodes are similar to important hubs in a network structure, which can directly determine the robustness and stability of the network. By effectively identifying and protecting these critical nodes, the robustness of the network can be improved, making it more resistant to external interference and attacks. There are various topology analysis methods for a given network, but key node identification methods often focus on either local attributes or global attributes. Designing an algorithm that combines both attributes can improve the accuracy of key node identification. In this paper, the constraint coefficient of a weakly connected network is calculated based on the Salton indicator, and a hierarchical tenacity global coefficient is obtained by an improved K-Shell decomposition method. Then, a hierarchical comprehensive key node identification algorithm is proposed which can comprehensively indicate the local and global attributes of the network nodes. Experimental results on real network datasets show that the proposed algorithm outperforms the other classic algorithms in terms of connectivity, average remaining edges, sensitivity and monotonicity.
Collapse
Affiliation(s)
- Fanshuo Qiu
- School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Chengpu Yu
- School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Yunji Feng
- School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Yao Li
- School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| |
Collapse
|
3
|
Yang G, Zhang Y, Lu Y, Xie Y, Yu J. Research on a Critical Link Discovery Method for Network Security Situational Awareness. ENTROPY (BASEL, SWITZERLAND) 2024; 26:315. [PMID: 38667869 PMCID: PMC11049312 DOI: 10.3390/e26040315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024]
Abstract
Network security situational awareness (NSSA) aims to capture, understand, and display security elements in large-scale network environments in order to predict security trends in the relevant network environment. With the internet's increasingly large scale, increasingly complex structure, and gradual diversification of components, the traditional single-layer network topology model can no longer meet the needs of network security analysis. Therefore, we conduct research based on a multi-layer network model for network security situational awareness, which is characterized by the three-layer network structure of a physical device network, a business application network, and a user role network. Its network characteristics require new assessment methods, so we propose a multi-layer network link importance assessment metric: the multi-layer-dependent link entropy (MDLE). On the one hand, the MDLE comprehensively evaluates the connectivity importance of links by fitting the link-local betweenness centrality and mapping entropy. On the other hand, it relies on the link-dependent mechanism to better aggregate the link importance contributions in each network layer. The experimental results show that the MDLE has better ordering monotonicity during critical link discovery and a higher destruction efficacy in destruction simulations compared to classical link importance metrics, thus better adapting to the critical link discovery requirements of a multi-layer network topology.
Collapse
Affiliation(s)
- Guozheng Yang
- College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China; (G.Y.); (Y.L.); (Y.X.); (J.Y.)
- Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China
| | - Yongheng Zhang
- College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China; (G.Y.); (Y.L.); (Y.X.); (J.Y.)
- Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China
| | - Yuliang Lu
- College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China; (G.Y.); (Y.L.); (Y.X.); (J.Y.)
- Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China
| | - Yi Xie
- College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China; (G.Y.); (Y.L.); (Y.X.); (J.Y.)
- Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China
| | - Jiayi Yu
- College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China; (G.Y.); (Y.L.); (Y.X.); (J.Y.)
- Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei 230037, China
| |
Collapse
|
4
|
Mrowinski MJ, Orzechowski KP, Fronczak A, Fronczak P. Interplay between tie strength and neighbourhood topology in complex networks. Sci Rep 2024; 14:7811. [PMID: 38565614 PMCID: PMC10987512 DOI: 10.1038/s41598-024-58357-4] [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: 02/12/2024] [Accepted: 03/28/2024] [Indexed: 04/04/2024] Open
Abstract
Granovetter's weak ties theory is a very important sociological theory according to which a correlation between edge weight and the network's topology should exist. More specifically, the neighbourhood overlap of two nodes connected by an edge should be positively correlated with edge weight (tie strength). However, some real social networks exhibit a negative correlation-the most prominent example is the scientific collaboration network, for which overlap decreases with edge weight. It has been demonstrated that the aforementioned inconsistency with Granovetter's theory can be alleviated in the scientific collaboration network through the use of asymmetric measures. In this paper, we explain that while asymmetric measures are often necessary to describe complex networks and to confirm Granovetter's theory, their interpretation is not simple, and there are pitfalls that one must be wary of. The definitions of asymmetric weights and overlaps introduce structural correlations that must be filtered out. We show that correlation profiles can be used to overcome this problem. Using this technique, not only do we confirm Granovetter's theory in various real and artificial social networks, but we also show that Granovetter-like weight-topology correlations are present in other complex networks (e.g. metabolic and neural networks). Our results suggest that Granovetter's theory is a sociological manifestation of more general principles governing various types of complex networks.
Collapse
Affiliation(s)
- Maciej J Mrowinski
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland.
| | - Kamil P Orzechowski
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland
| | - Agata Fronczak
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland
| | - Piotr Fronczak
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Zhang J, Zhao L, Sun P, Liang W. Dynamic identification of important nodes in complex networks based on the KPDN-INCC method. Sci Rep 2024; 14:5814. [PMID: 38461316 PMCID: PMC10924965 DOI: 10.1038/s41598-024-56226-8] [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: 12/11/2023] [Accepted: 03/04/2024] [Indexed: 03/11/2024] Open
Abstract
This article focuses on the cascading failure problem and node importance evaluation method in complex networks. To address the issue of identifying important nodes in dynamic networks, the method used in static networks is introduced and the necessity of re-evaluating node status during node removal is proposed. Studies have found that the methods for identifying dynamic and static network nodes are two different directions, and most literature only uses dynamic methods to verify static methods. Therefore, it is necessary to find suitable node evaluation methods for dynamic networks. Based on this, this article proposes a method that integrates local and global correlation properties. In terms of global features, we introduce an improved k-shell method with fusion degree to improve the resolution of node ranking. In terms of local features, we introduce Solton factor and structure hole factor improved by INCC (improved network constraint coefficient), which effectively improves the algorithm's ability to identify the relationship between adjacent nodes. Through comparison with existing methods, it is found that the KPDN-INCC method proposed in this paper complements the KPDN method and can accurately identify important nodes, thereby helping to quickly disintegrate the network. Finally, the effectiveness of the proposed method in identifying important nodes in a small-world network with a random parameter less than 0.4 was verified through artificial network experiments.
Collapse
Affiliation(s)
- Jieyong Zhang
- Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China
| | - Liang Zhao
- No. 96872 Troops of PLA, Baoji, 721000, China
| | - Peng Sun
- Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China.
| | - Wei Liang
- Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China
| |
Collapse
|
8
|
Sheng A, Su Q, Li A, Wang L, Plotkin JB. Constructing temporal networks with bursty activity patterns. Nat Commun 2023; 14:7311. [PMID: 37951967 PMCID: PMC10640578 DOI: 10.1038/s41467-023-42868-1] [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: 04/13/2023] [Accepted: 10/24/2023] [Indexed: 11/14/2023] Open
Abstract
Human social interactions tend to vary in intensity over time, whether they are in person or online. Variable rates of interaction in structured populations can be described by networks with the time-varying activity of links and nodes. One of the key statistics to summarize temporal patterns is the inter-event time, namely the duration between successive pairwise interactions. Empirical studies have found inter-event time distributions that are heavy-tailed, for both physical and digital interactions. But it is difficult to construct theoretical models of time-varying activity on a network that reproduce the burstiness seen in empirical data. Here we develop a spanning-tree method to construct temporal networks and activity patterns with bursty behavior. Our method ensures any desired target inter-event time distributions for individual nodes and links, provided the distributions fulfill a consistency condition, regardless of whether the underlying topology is static or time-varying. We show that this model can reproduce burstiness found in empirical datasets, and so it may serve as a basis for studying dynamic processes in real-world bursty interactions.
Collapse
Affiliation(s)
- Anzhi Sheng
- Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China
- Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qi Su
- Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
- Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai, 200240, China
| | - Aming Li
- Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
| | - Long Wang
- Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
| | - Joshua B Plotkin
- Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for Mathematical Biology, University of Pennsylvania, Philadelphia, PA, 19014, USA.
| |
Collapse
|
9
|
Jia JS, Li Y, Liu S, Christakis NA, Jia J. Emergency communications after earthquake reveal social network backbone of important ties. PNAS NEXUS 2023; 2:pgad358. [PMID: 38024411 PMCID: PMC10658761 DOI: 10.1093/pnasnexus/pgad358] [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: 03/23/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023]
Abstract
Social networks provide a basis for collective resilience to disasters. Combining the quasi-experimental context of a major earthquake in Ya'an, China, with anonymized mobile telecommunications records regarding 91,839 Ya'an residents, we use initial bursts of postdisaster communications (e.g. choice of alter, order of calls, and latency) to reveal the "important ties" that form the social network backbone. We find that only 26.8% of important ties activated during the earthquake were the strongest ties during normal times. Many important ties were hitherto latent and weak, only to become persistent and strong after the earthquake. We show that which ties activated during a sudden disaster are best predicted by the interaction of embeddedness and tie strength. Moreover, a backbone of important ties alone (without the inclusion of weak ties ordinarily seen as important to bridge communities) is sufficient to generate a hierarchical structure of social networks that connect a disaster zone's disparate communities.
Collapse
Affiliation(s)
- Jayson S Jia
- Faculty of Business and Economics, The University of Hong Kong, Hong Kong SAR, China
| | - Yiwei Li
- Department of Marketing & International Business, Faculty of Business, Lingnan University, Hong Kong SAR, China
| | - Sheng Liu
- Department of Marketing & International Business, Faculty of Business, Lingnan University, Hong Kong SAR, China
| | | | - Jianmin Jia
- Shenzhen Finance Institute, School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China
| |
Collapse
|
10
|
Li W, Zhu Y, Xia C. Evolutionary dynamics of N-player sender-receiver game in networks with community structure. CHAOS (WOODBURY, N.Y.) 2023; 33:103117. [PMID: 37831798 DOI: 10.1063/5.0157761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
Network typology largely affects the evolutionary dynamics of collective behaviors in many real-world complex systems. As a conventional transmission model, the sender-receiver game paves the way to explore the evolution of honest signals between senders and receivers. In practice, the utilities of an agent often depend not only on pairwise interactions, but also on the group influence of players around them, and thus there is an urgent need for deeper theoretical modeling and investigations on individuals' non-pairwise interactions. Considering the underlying evolutionary game dynamics and multiple community network structures, we explore the evolution of honest behaviors by extending the sender-receiver game to multiple communities. With the new dynamical model of the multi-community system, we perform a stability analysis of the system equilibrium state. Our results reveal the condition to promote the evolution of honest behaviors and provide an effective method for enhancing collaboration behaviors in distributed complex systems. Current results help us to deeply understand how collective decision-making behaviors evolve, influenced by the spread of true information and misinformation in large dynamic systems.
Collapse
Affiliation(s)
- Wenbo Li
- Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China
| | - Yuying Zhu
- School of Artificial Intelligence, Tiangong University, Tianjin 300387, China
| | - Chengyi Xia
- School of Artificial Intelligence, Tiangong University, Tianjin 300387, China
| |
Collapse
|
11
|
Su Q, McAvoy A, Plotkin JB. Strategy evolution on dynamic networks. NATURE COMPUTATIONAL SCIENCE 2023; 3:763-776. [PMID: 38177777 DOI: 10.1038/s43588-023-00509-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 08/08/2023] [Indexed: 01/06/2024]
Abstract
Models of strategy evolution on static networks help us understand how population structure can promote the spread of traits like cooperation. One key mechanism is the formation of altruistic spatial clusters, where neighbors of a cooperative individual are likely to reciprocate, which protects prosocial traits from exploitation. However, most real-world interactions are ephemeral and subject to exogenous restructuring, so that social networks change over time. Strategic behavior on dynamic networks is difficult to study, and much less is known about the resulting evolutionary dynamics. Here we provide an analytical treatment of cooperation on dynamic networks, allowing for arbitrary spatial and temporal heterogeneity. We show that transitions among a large class of network structures can favor the spread of cooperation, even if each individual social network would inhibit cooperation when static. Furthermore, we show that spatial heterogeneity tends to inhibit cooperation, whereas temporal heterogeneity tends to promote it. Dynamic networks can have profound effects on the evolution of prosocial traits, even when individuals have no agency over network structures.
Collapse
Affiliation(s)
- Qi Su
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.
- Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai, China.
| | - Alex McAvoy
- School of Data Science and Society, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Joshua B Plotkin
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Mathematical Biology, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
12
|
Bröhl T, von Wrede R, Lehnertz K. Impact of biological rhythms on the importance hierarchy of constituents in time-dependent functional brain networks. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1237004. [PMID: 37705698 PMCID: PMC10497113 DOI: 10.3389/fnetp.2023.1237004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023]
Abstract
Biological rhythms are natural, endogenous cycles with period lengths ranging from less than 24 h (ultradian rhythms) to more than 24 h (infradian rhythms). The impact of the circadian rhythm (approximately 24 h) and ultradian rhythms on spectral characteristics of electroencephalographic (EEG) signals has been investigated for more than half a century. Yet, only little is known on how biological rhythms influence the properties of EEG-derived evolving functional brain networks. Here, we derive such networks from multiday, multichannel EEG recordings and use different centrality concepts to assess the time-varying importance hierarchy of the networks' vertices and edges as well as the various aspects of their structural integration in the network. We observe strong circadian and ultradian influences that highlight distinct subnetworks in the evolving functional brain networks. Our findings indicate the existence of a vital and fundamental subnetwork that is rather generally involved in ongoing brain activities during wakefulness and sleep.
Collapse
Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
| |
Collapse
|
13
|
Iñiguez G, Heydari S, Kertész J, Saramäki J. Universal patterns in egocentric communication networks. Nat Commun 2023; 14:5217. [PMID: 37633934 PMCID: PMC10460427 DOI: 10.1038/s41467-023-40888-5] [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: 03/16/2023] [Accepted: 08/15/2023] [Indexed: 08/28/2023] Open
Abstract
Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at the network and individual levels. Egocentric networks, networks of relationships around an individual, exhibit few strong ties and more weaker ties, as evidenced by electronic communication records. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and driving mechanisms of social tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets of interactions between millions of people during months to years. We find universality in tie strength distributions and their individual-level variation across communication modes, even in channels not reflecting offline social relationships. Via a simple model of egocentric network evolution, we show that the observed universality arises from the competition between cumulative advantage and random choice, two tie reinforcement mechanisms whose balance determines the diversity of tie strengths. Our results provide insight into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.
Collapse
Affiliation(s)
- Gerardo Iñiguez
- Department of Network and Data Science, Central European University, 1100, Vienna, Austria.
- Department of Computer Science, Aalto University School of Science, 00076, Aalto, Finland.
- Faculty of Information Technology and Communication Sciences, Tampere University, 33720, Tampere, Finland.
- Centro de Ciencias de la Complejidad, Universidad Nacional Autonóma de México, 04510, Ciudad de México, Mexico.
| | - Sara Heydari
- Department of Computer Science, Aalto University School of Science, 00076, Aalto, Finland
| | - János Kertész
- Department of Network and Data Science, Central European University, 1100, Vienna, Austria
- Complexity Science Hub, 1080, Vienna, Austria
| | - Jari Saramäki
- Department of Computer Science, Aalto University School of Science, 00076, Aalto, Finland.
| |
Collapse
|
14
|
Bokányi E, Heemskerk EM, Takes FW. The anatomy of a population-scale social network. Sci Rep 2023; 13:9209. [PMID: 37280385 DOI: 10.1038/s41598-023-36324-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/01/2023] [Indexed: 06/08/2023] Open
Abstract
Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level.
Collapse
|
15
|
Bröhl T, Lehnertz K. A perturbation-based approach to identifying potentially superfluous network constituents. CHAOS (WOODBURY, N.Y.) 2023; 33:2894464. [PMID: 37276550 DOI: 10.1063/5.0152030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/16/2023] [Indexed: 06/07/2023]
Abstract
Constructing networks from empirical time-series data is often faced with the as yet unsolved issue of how to avoid potentially superfluous network constituents. Such constituents can result, e.g., from spatial and temporal oversampling of the system's dynamics, and neglecting them can lead to severe misinterpretations of network characteristics ranging from global to local scale. We derive a perturbation-based method to identify potentially superfluous network constituents that makes use of vertex and edge centrality concepts. We investigate the suitability of our approach through analyses of weighted small-world, scale-free, random, and complete networks.
Collapse
Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany
| |
Collapse
|
16
|
Xi Y, Cui X. Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25050754. [PMID: 37238509 DOI: 10.3390/e25050754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/28/2023]
Abstract
Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes. Graph neural networks (GNNs) have emerged as a prominent deep learning architecture, capable of efficiently aggregating node information and discerning node influence. However, existing graph neural networks often ignore the strength of the relationships between nodes when aggregating information about neighboring nodes. In complex networks, neighboring nodes often do not have the same influence on the target node, so the existing graph neural network methods are not effective. In addition, the diversity of complex networks also makes it difficult to adapt node features with a single attribute to different types of networks. To address the above problems, the paper constructs node input features using information entropy combined with the node degree value and the average degree of the neighbor, and proposes a simple and effective graph neural network model. The model obtains the strength of the relationships between nodes by considering the degree of neighborhood overlap, and uses this as the basis for message passing, thereby effectively aggregating information about nodes and their neighborhoods. Experiments are conducted on 12 real networks, using the SIR model to verify the effectiveness of the model with the benchmark method. The experimental results show that the model can identify the influence of nodes in complex networks more effectively.
Collapse
Affiliation(s)
- Ying Xi
- Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
| | - Xiaohui Cui
- Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
| |
Collapse
|
17
|
Ruiz-García M, Ozaita J, Pereda M, Alfonso A, Brañas-Garza P, Cuesta JA, Sánchez A. Triadic influence as a proxy for compatibility in social relationships. Proc Natl Acad Sci U S A 2023; 120:e2215041120. [PMID: 36947512 PMCID: PMC10068781 DOI: 10.1073/pnas.2215041120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 02/14/2023] [Indexed: 03/23/2023] Open
Abstract
Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relationships, including tests for personal traits of all the students. We introduce a metric-the "triadic influence"-that measures the influence of nearest neighbors in the relationships of their contacts. We use neural networks to predict the sign of the relationships in these social networks, extracting the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, using the triadic influence (a simple one-dimensional metric) achieves the best accuracy, and adding the personal traits of the students does not improve the results, suggesting that the triadic influence acts as a proxy for the social compatibility of students. We postulate that the probabilities extracted from the neural networks-functions of the triadic influence and the personalities of the students-control the evolution of real social networks, opening an avenue for the quantitative study of these systems.
Collapse
Affiliation(s)
- Miguel Ruiz-García
- Departamento de Estructura de la Materia, Física Térmica y Electrónica, Universidad Complutense Madrid, Madrid28040, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid28911, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés28911, Spain
| | - Juan Ozaita
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés28911, Spain
| | - María Pereda
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid28911, Spain
- Grupo de Investigación Ingeniería de Organización y Logística (IOL), Departamento Ingeniería de Organización, Administración de empresas y Estadística, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid28006, Spain
| | - Antonio Alfonso
- LoyolaBehLAB, Department of Economics and Fundación ETEA, Universidad Loyola Andalucía, Córdoba14004, Spain
| | - Pablo Brañas-Garza
- LoyolaBehLAB, Department of Economics and Fundación ETEA, Universidad Loyola Andalucía, Córdoba14004, Spain
| | - José A. Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid28911, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés28911, Spain
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, Zaragoza50018, Spain
| | - Angel Sánchez
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid28911, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés28911, Spain
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, Zaragoza50018, Spain
| |
Collapse
|
18
|
Han L, Lin Z, Tang M, Liu Y, Guan S. Impact of human contact patterns on epidemic spreading in time-varying networks. Phys Rev E 2023; 107:024312. [PMID: 36932475 DOI: 10.1103/physreve.107.024312] [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: 08/14/2022] [Accepted: 02/06/2023] [Indexed: 03/19/2023]
Abstract
Human contact behaviors involve both dormant and active processes. The dormant (active) process goes from the disappearance (creation) to the creation (disappearance) of an edge. The dormant (active) time is the elapsed time since the edge became dormant (active). Many empirical studies have revealed that dormant and active times in human contact behaviors tend to show a long-tailed distribution. Previous researches focused on the impact of the dormant process on spreading dynamics. However, the epidemic spreading happens on the active process. This raises the question of how the active process affects epidemic spreading in complex networks. Here, we propose a novel time-varying network model in which the distributions of both the dormant time and active time of edges are adjustable. We develop a pairwise approximation method to describe the spreading dynamical processes in the time-varying networks. Through extensive numerical simulations, we find that the epidemic threshold is proportional to the mean dormant time and inversely proportional to the mean active time. The attack rate decreases with the increase of mean dormant time and increases with the increase of mean active time. It is worth noting that the epidemic threshold and the attack rate (e.g., the infected density in the steady state) are independent of the heterogeneities of the dormant time distribution and the active time distribution. Increasing the heterogeneity of the dormant time distribution accelerates epidemic spreading while increasing the heterogeneity of the active time distribution slows it down.
Collapse
Affiliation(s)
- Lilei Han
- School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
| | - Zhaohua Lin
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China
| | - Ming Tang
- School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
| | - Ying Liu
- School of Computer Science, Southwest Petroleum University, Chengdu 610500, China
| | - Shuguang Guan
- School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
| |
Collapse
|
19
|
Adamovich T, Zakharov I, Tabueva A, Malykh S. The thresholding problem and variability in the EEG graph network parameters. Sci Rep 2022; 12:18659. [PMID: 36333413 PMCID: PMC9636266 DOI: 10.1038/s41598-022-22079-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph's global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.
Collapse
Affiliation(s)
- Timofey Adamovich
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Ilya Zakharov
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Anna Tabueva
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Sergey Malykh
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| |
Collapse
|
20
|
Tang MC, Jung YE, LI Y. Exploring the sociotechnical system of Chinese internet literature online forums: a social network analytical approach. ONLINE INFORMATION REVIEW 2022. [DOI: 10.1108/oir-11-2021-0596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeChinese internet literature (CIL) platforms afford freedom for creative expression and opportunities for direct interactions between writers and fans and among fans. Enabled by these platforms' technological and commercial arrangement, a new form of literary production and consumption has emerged, the most significant of which is the role of fans participation. A social network analysis of the interaction patterns in online fan communities was conducted to investigate fan communication activities at scale. Of particular interest is how the socio-technical system of the site influences its network topology.Design/methodology/approachOnline forums for 10 popular fiction titles in Qidian, the leading CIL platform, were analyzed. Social networks were constructed based on a post–reply–reply threaded discussion structure. Various aspects of fan interactions were analyzed, including number of replies per post, post length and emerging network patterns.FindingsSimilarities in network topology shared by CIL fan forums and other online communities, such as small-world and scale properties, were discovered; however, distinct network dynamics were also identified. Consistent with previous findings, writers and moderators, along with a few highly ranked fans, occupied the central positions in the network. This was due to their social roles and the nature of their posts rather than, as the conventional explanation goes, preferential attachment.Originality/valueThe findings demonstrate how community-specific circumstances and norms influence interaction patterns and the resultant network structure. It was revealed that in the CIL sites, the users adopted the technologies in unexpected ways. And the resulting network topology can be attributed to the interplay between the sites' official arrangement and users' adaptive tactics.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2021-0596.
Collapse
|
21
|
Illari L, Restrepo NJ, Johnson NF. Losing the battle over best-science guidance early in a crisis: COVID-19 and beyond. SCIENCE ADVANCES 2022; 8:eabo8017. [PMID: 36170371 PMCID: PMC9519035 DOI: 10.1126/sciadv.abo8017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 08/11/2022] [Indexed: 06/16/2023]
Abstract
Ensuring widespread public exposure to best-science guidance is crucial in any crisis, e.g., coronavirus disease 2019 (COVID-19), monkeypox, abortion misinformation, climate change, and beyond. We show how this battle got lost on Facebook very early during the COVID-19 pandemic and why the mainstream majority, including many parenting communities, had already moved closer to more extreme communities by the time vaccines arrived. Hidden heterogeneities in terms of who was talking and listening to whom explain why Facebook's own promotion of best-science guidance also appears to have missed key audience segments. A simple mathematical model reproduces the exposure dynamics at the system level. Our findings could be used to tailor guidance at scale while accounting for individual diversity and to help predict tipping point behavior and system-level responses to interventions in future crises.
Collapse
Affiliation(s)
- Lucia Illari
- Physics Department, George Washington University, Washington, D.C. 20052, USA
| | | | - Neil F. Johnson
- Physics Department, George Washington University, Washington, D.C. 20052, USA
| |
Collapse
|
22
|
Monitoring supply networks from mobile phone data for estimating the systemic risk of an economy. Sci Rep 2022; 12:13347. [PMID: 35922453 PMCID: PMC9349293 DOI: 10.1038/s41598-022-13104-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 05/20/2022] [Indexed: 11/09/2022] Open
Abstract
Remarkably little is known about the structure, formation, and dynamics of supply- and production networks that form one foundation of society. Neither the resilience of these networks is known, nor do we have ways to systematically monitor their ongoing change. Systemic risk contributions of individual companies were hitherto not quantifiable since data on supply networks on the firm-level do not exist with the exception of a very few countries. Here we use telecommunication meta data to reconstruct nationwide firm-level supply networks in almost real-time. We find the probability of observing a supply-link, given the existence of a strong communication-link between two companies, to be about 90%. The so reconstructed supply networks allow us to reliably quantify the systemic risk of individual companies and thus obtain an estimate for a country’s economic resilience. We identify about 65 companies, from a broad range of company sizes and from 22 different industry sectors, that could potentially cause massive damages. The method can be used for objectively monitoring change in production processes which might become essential during the green transition.
Collapse
|
23
|
Pérez-Martínez H, Bauzá FJ, Soriano-Paños D, Gómez-Gardeñes J, Floría LM. Emergence, survival, and segregation of competing gangs. CHAOS (WOODBURY, N.Y.) 2022; 32:083114. [PMID: 36049916 DOI: 10.1063/5.0084972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
In this paper, we approach the phenomenon of criminal activity from an infectious perspective by using tailored compartmental agent-based models that include the social flavor of the mechanisms governing the evolution of crime in society. Specifically, we focus on addressing how the existence of competing gangs shapes the penetration of crime. The mean-field analysis of the model proves that the introduction of dynamical rules favoring the simultaneous survival of both gangs reduces the overall number of criminals across the population as a result of the competition between them. The implementation of the model in networked populations with homogeneous contact patterns reveals that the evolution of crime substantially differs from that predicted by the mean-field equations. We prove that the system evolves toward a segregated configuration where, depending on the features of the underlying network, both gangs can form spatially separated clusters. In this scenario, we show that the beneficial effect of the coexistence of two gangs is hindered, resulting in a higher penetration of crime in the population.
Collapse
Affiliation(s)
- H Pérez-Martínez
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
| | - F J Bauzá
- GOTHAM lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
| | - D Soriano-Paños
- GOTHAM lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
| | - J Gómez-Gardeñes
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
| | - L M Floría
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
| |
Collapse
|
24
|
Network structure from a characterization of interactions in complex systems. Sci Rep 2022; 12:11742. [PMID: 35817803 PMCID: PMC9273794 DOI: 10.1038/s41598-022-14397-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/06/2022] [Indexed: 11/29/2022] Open
Abstract
Many natural and man-made complex dynamical systems can be represented by networks with vertices representing system units and edges the coupling between vertices. If edges of such a structural network are inaccessible, a widely used approach is to identify them with interactions between vertices, thereby setting up a functional network. However, it is an unsolved issue if and to what extent important properties of a functional network on the global and the local scale match those of the corresponding structural network. We address this issue by deriving functional networks from characterizing interactions in paradigmatic oscillator networks with widely-used time-series-analysis techniques for various factors that alter the collective network dynamics. Surprisingly, we find that particularly key constituents of functional networks—as identified with betweenness and eigenvector centrality—coincide with ground truth to a high degree, while global topological and spectral properties—clustering coefficient, average shortest path length, assortativity, and synchronizability—clearly deviate. We obtain similar concurrences for an empirical network. Our findings are of relevance for various scientific fields and call for conceptual and methodological refinements to further our understanding of the relationship between structure and function of complex dynamical systems.
Collapse
|
25
|
Roy C, Bhattacharya K, Dunbar RIM, Kaski K. Turnover in close friendships. Sci Rep 2022; 12:11018. [PMID: 35773294 PMCID: PMC9247060 DOI: 10.1038/s41598-022-15070-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 06/17/2022] [Indexed: 11/23/2022] Open
Abstract
Humans are social animals and the interpersonal bonds formed between them are crucial for their development and well being in a society. These relationships are usually structured into several layers (Dunbar’s layers of friendship) depending on their significance in an individual’s life with closest friends and family being the most important ones taking major part of their time and communication effort. However, we have little idea how the initiation and termination of these relationships occurs across the lifespan. Mobile phones, in particular, have been used extensively to shed light on the different types of social interactions between individuals and to explore this, we analyse a national cellphone database to determine how and when changes in close relationships occur in the two genders. In general, membership of this inner circle of intimate relationships is extremely stable, at least over a three-year period. However, around 1–4% of alters change every year, with the rate of change being higher among 17-21 year olds than older adults. Young adult females terminate more of their opposite-gender relationships, while older males are more persistent in trying to maintain relationships in decline. These results emphasise the variability in relationship dynamics across age and gender, and remind us that individual differences play an important role in the structure of social networks. Overall, our study provides a holistic understanding of the dynamic nature of close relationships during different stages of human life.
Collapse
Affiliation(s)
- Chandreyee Roy
- Department of Computer Science, Aalto University School of Science, Espoo, Finland.
| | - Kunal Bhattacharya
- Department of Computer Science, Aalto University School of Science, Espoo, Finland.,Department of Industrial Engineering and Management, Aalto University School of Science, Espoo, Finland
| | - Robin I M Dunbar
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Kimmo Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, Finland.,The Alan Turing Institute, London, UK
| |
Collapse
|
26
|
Mijatovic G, Kljajic D, Kasas-Lazetic K, Milutinov M, Stivala S, Busacca A, Cino AC, Stramaglia S, Faes L. Information Dynamics of Electric Field Intensity before and during the COVID-19 Pandemic. ENTROPY 2022; 24:e24050726. [PMID: 35626609 PMCID: PMC9140641 DOI: 10.3390/e24050726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/08/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022]
Abstract
This work investigates the temporal statistical structure of time series of electric field (EF) intensity recorded with the aim of exploring the dynamical patterns associated with periods with different human activity in urban areas. The analyzed time series were obtained from a sensor of the EMF RATEL monitoring system installed in the campus area of the University of Novi Sad, Serbia. The sensor performs wideband cumulative EF intensity monitoring of all active commercial EF sources, thus including those linked to human utilization of wireless communication systems. Monitoring was performed continuously during the years 2019 and 2020, allowing us to investigate the effects on the patterns of EF intensity of varying conditions of human mobility, including regular teaching and exam activity within the campus, as well as limitations to mobility related to the COVID-19 pandemic. Time series analysis was performed using both simple statistics (mean and variance) and combining the information-theoretic measure of information storage (IS) with the method of surrogate data to quantify the regularity of EF dynamic patterns and detect the presence of nonlinear dynamics. Moreover, to assess the possible coexistence of dynamic behaviors across multiple temporal scales, IS analysis was performed over consecutive observation windows lasting one day, week, month, and year, respectively coarse grained at time scales of 6 min, 30 min, 2 h, and 1 day. Our results document that the EF intensity patterns of variability are modulated by the movement of people at daily, weekly, and monthly scales, and are blunted during periods of restricted mobility related to the COVID-19 pandemic. Mobility restrictions also affected significantly the regularity of the EF intensity time series, resulting in lower values of IS observed simultaneously with a loss of nonlinear dynamics. Thus, our analysis can be useful to investigate changes in the global patterns of human mobility both during pandemics or other types of events, and from this perspective may serve to implement strategies for safety assessment and for optimizing the design of networks of EF sensors.
Collapse
Affiliation(s)
- Gorana Mijatovic
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia; (G.M.); (D.K.); (K.K.-L.); (M.M.)
| | - Dragan Kljajic
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia; (G.M.); (D.K.); (K.K.-L.); (M.M.)
| | - Karolina Kasas-Lazetic
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia; (G.M.); (D.K.); (K.K.-L.); (M.M.)
| | - Miodrag Milutinov
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia; (G.M.); (D.K.); (K.K.-L.); (M.M.)
| | - Salvatore Stivala
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (S.S.); (A.B.); (A.C.C.)
| | - Alessandro Busacca
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (S.S.); (A.B.); (A.C.C.)
| | - Alfonso Carmelo Cino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (S.S.); (A.B.); (A.C.C.)
| | | | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (S.S.); (A.B.); (A.C.C.)
- Correspondence:
| |
Collapse
|
27
|
Zhang Y, Lu Y, Yang G. Link importance assessment strategy based on improved k-core decomposition in complex networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:7019-7031. [PMID: 35730294 DOI: 10.3934/mbe.2022331] [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: 06/15/2023]
Abstract
Improving the effectiveness of target link importance assessment strategy has become an important research direction within the field of complex networks today. The reasearch shows that the link importance assessment strategy based on betweenness centrality is the current optimal solution, but its high computational complexity makes it difficult to meet the application requirements of large-scale networks. The k-core decomposition method, as a theoretical tool that can effectively analyze and characterize the topological properties of complex networks and systems, has been introduced to facilitate the generation of link importance assessment strategy and, based on this, a link importance assessment indicator link shell has been developed. The strategy achieves better results in numerical simulations. In this study, we incorporated topological overlap theory to further optimize the attack effect and propose a new link importance assessment indicator link topological shell called t-shell. Simulations using real world networks and scale-free networks show that t-shell based target link importance assessment strategies perform better than shell based strategies without increasing the computational complexity; this can provide new ideas for the study of large-scale network destruction strategies.
Collapse
Affiliation(s)
- Yongheng Zhang
- Electronic Engineering Institute, National University of Defense Technology, Heifei 230037, China
| | - Yuliang Lu
- Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, China
| | - GuoZheng Yang
- Electronic Engineering Institute, National University of Defense Technology, Heifei 230037, China
| |
Collapse
|
28
|
Reme BA, Kotsadam A, Bjelland J, Sundsøy PR, Lind JT. Quantifying social segregation in large-scale networks. Sci Rep 2022; 12:6474. [PMID: 35440681 PMCID: PMC9018874 DOI: 10.1038/s41598-022-10273-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/17/2022] [Indexed: 11/30/2022] Open
Abstract
We present a measure of social segregation which combines mobile phone data and income register data in Oslo, Norway. In addition to measuring the extent of social segregation, our study shows that social segregation is strong, robust, and that social networks are particularly clustered among the richest. Using location data on the areas where people work, we also examine whether exposure to other social strata weakens measured segregation. Lastly, we extend our analysis to a large South Asian city and show that our main results hold across two widely different societies.
Collapse
Affiliation(s)
- Bjørn-Atle Reme
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
| | | | | | | | - Jo Thori Lind
- Department of Economics, University of Oslo, Oslo, Norway
| |
Collapse
|
29
|
Majhi S, Rakshit S, Ghosh D. Oscillation suppression and chimera states in time-varying networks. CHAOS (WOODBURY, N.Y.) 2022; 32:042101. [PMID: 35489845 DOI: 10.1063/5.0087291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
Complex network theory has offered a powerful platform for the study of several natural dynamic scenarios, based on the synergy between the interaction topology and the dynamics of its constituents. With research in network theory being developed so fast, it has become extremely necessary to move from simple network topologies to more sophisticated and realistic descriptions of the connectivity patterns. In this context, there is a significant amount of recent works that have emerged with enormous evidence establishing the time-varying nature of the connections among the constituents in a large number of physical, biological, and social systems. The recent review article by Ghosh et al. [Phys. Rep. 949, 1-63 (2022)] demonstrates the significance of the analysis of collective dynamics arising in temporal networks. Specifically, the authors put forward a detailed excerpt of results on the origin and stability of synchronization in time-varying networked systems. However, among the complex collective dynamical behaviors, the study of the phenomenon of oscillation suppression and that of other diverse aspects of synchronization are also considered to be central to our perception of the dynamical processes over networks. Through this review, we discuss the principal findings from the research studies dedicated to the exploration of the two collective states, namely, oscillation suppression and chimera on top of time-varying networks of both static and mobile nodes. We delineate how temporality in interactions can suppress oscillation and induce chimeric patterns in networked dynamical systems, from effective analytical approaches to computational aspects, which is described while addressing these two phenomena. We further sketch promising directions for future research on these emerging collective behaviors in time-varying networks.
Collapse
Affiliation(s)
- Soumen Majhi
- Department of Mathematics, Bar-Ilan University, Ramat-Gan 5290002, Israel
| | - Sarbendu Rakshit
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - Dibakar Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| |
Collapse
|
30
|
Xu YZ, Po HF, Yeung CH, Saad D. Scalable node-disjoint and edge-disjoint multiwavelength routing. Phys Rev E 2022; 105:044316. [PMID: 35590677 DOI: 10.1103/physreve.105.044316] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 04/07/2022] [Indexed: 06/15/2023]
Abstract
Probabilistic message-passing algorithms are developed for routing transmissions in multiwavelength optical communication networks, under node- and edge-disjoint routing constraints and for various objective functions. Global routing optimization is a hard computational task on its own but is made much more difficult under the node- and edge-disjoint constraints and in the presence of multiple wavelengths, a problem which dominates routing efficiency in real optical communication networks that carry most of the world's internet traffic. The scalable principled method we have developed is exact on trees but provides good approximate solutions on locally treelike graphs. It accommodates a variety of objective functions that correspond to low latency, load balancing, and consolidation of routes and can be easily extended to include heterogeneous signal-to-noise values on edges and a restriction on the available wavelengths per edge. It can be used for routing and managing transmissions on existing topologies as well as for designing and modifying optical communication networks. Additionally, it provides the tool for settling an open and much-debated question on the merit of wavelength-switching nodes and the added capabilities they provide. The methods have been tested on generated networks such as random-regular, Erdős Rényi, and power-law graphs, as well as on optical communication networks in the United Kingdom and United States. They show excellent performance with respect to existing methodology on small networks and have been scaled up to network sizes that are beyond the reach of most existing algorithms.
Collapse
Affiliation(s)
- Yi-Zhi Xu
- The Nonlinearity and Complexity Research Group, Aston University, Birmingham B4 7ET, United Kingdom
| | - Ho Fai Po
- Department of Science and Environmental Studies, The Education University of Hong Kong, 10 Lo Ping Road, Taipo, Hong Kong
| | - Chi Ho Yeung
- Department of Science and Environmental Studies, The Education University of Hong Kong, 10 Lo Ping Road, Taipo, Hong Kong
| | - David Saad
- The Nonlinearity and Complexity Research Group, Aston University, Birmingham B4 7ET, United Kingdom
| |
Collapse
|
31
|
Fronczak A, Mrowinski MJ, Fronczak P. Scientific success from the perspective of the strength of weak ties. Sci Rep 2022; 12:5074. [PMID: 35332225 PMCID: PMC8948253 DOI: 10.1038/s41598-022-09118-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 01/25/2022] [Indexed: 11/27/2022] Open
Abstract
We present the first complete verification of Granovetter’s theory of social networks using a massive dataset, i.e. DBLP computer science bibliography database. For this purpose, we study a coauthorship network, which is considered one of the most important examples that contradicts the universality of this theory. We achieve this goal by rejecting the assumption of the symmetry of social ties. Our approach is grounded in well-established heterogeneous (degree-based) mean-field theory commonly used to study dynamical processes on complex networks. Granovetter’s theory is based on two hypotheses that assign different roles to interpersonal, information-carrying connections. The first hypothesis states that strong ties carrying the majority of interaction events are located mainly within densely connected groups of people. The second hypothesis maintains that these groups are connected by sparse weak ties that are of vital importance for the diffusion of information—individuals who have access to weak ties have an advantage over those who do not. Given the scientific collaboration network, with strength of directed ties measured by the asymmetric fraction of joint publications, we show that scientific success is strongly correlated with the structure of a scientist’s collaboration network. First, among two scientists, with analogous achievements, the one with weaker ties tends to have the higher h-index, and second, teams connected by such ties create more cited publications.
Collapse
Affiliation(s)
- Agata Fronczak
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland.
| | - Maciej J Mrowinski
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland
| | - Piotr Fronczak
- Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland
| |
Collapse
|
32
|
A straightforward edge centrality concept derived from generalizing degree and strength. Sci Rep 2022; 12:4407. [PMID: 35292696 PMCID: PMC8922089 DOI: 10.1038/s41598-022-08254-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/03/2022] [Indexed: 12/24/2022] Open
Abstract
Vertex degree—the number of edges that are incident to a vertex—is a fundamental concept in network theory. It is the historically first and conceptually simplest centrality concept to rate the importance of a vertex for a network’s structure and dynamics. Unlike many other centrality concepts, for which joint metrics have been proposed for both vertices and edges, by now there is no concept for an edge centrality analogous to vertex degree. Here, we propose such a concept—termed nearest-neighbor edge centrality—and demonstrate its suitability for a non-redundant identification of central edges in paradigmatic network models as well as in real-world networks from various scientific domains.
Collapse
|
33
|
Mimar S, Ghoshal G. A sampling-guided unsupervised learning method to capture percolation in complex networks. Sci Rep 2022; 12:4147. [PMID: 35264699 PMCID: PMC8907239 DOI: 10.1038/s41598-022-07921-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 02/28/2022] [Indexed: 11/09/2022] Open
Abstract
The use of machine learning methods in classical and quantum systems has led to novel techniques to classify ordered and disordered phases, as well as uncover transition points in critical phenomena. Efforts to extend these methods to dynamical processes in complex networks is a field of active research. Network-percolation, a measure of resilience and robustness to structural failures, as well as a proxy for spreading processes, has numerous applications in social, technological, and infrastructural systems. A particular challenge is to identify the existence of a percolation cluster in a network in the face of noisy data. Here, we consider bond-percolation, and introduce a sampling approach that leverages the core-periphery structure of such networks at a microscopic scale, using onion decomposition, a refined version of the k-core. By selecting subsets of nodes in a particular layer of the onion spectrum that follow similar trajectories in the percolation process, percolating phases can be distinguished from non-percolating ones through an unsupervised clustering method. Accuracy in the initial step is essential for extracting samples with information-rich content, that are subsequently used to predict the critical transition point through the confusion scheme, a recently introduced learning method. The method circumvents the difficulty of missing data or noisy measurements, as it allows for sampling nodes from both the core and periphery, as well as intermediate layers. We validate the effectiveness of our sampling strategy on a spectrum of synthetic network topologies, as well as on two real-word case studies: the integration time of the US domestic airport network, and the identification of the epidemic cluster of COVID-19 outbreaks in three major US states. The method proposed here allows for identifying phase transitions in empirical time-varying networks.
Collapse
Affiliation(s)
- Sayat Mimar
- Department of Physics and Astronomy, University of Rochester, Rochester, 14627, NY, USA
| | - Gourab Ghoshal
- Department of Physics and Astronomy, University of Rochester, Rochester, 14627, NY, USA. .,Department of Computer Science, University of Rochester, Rochester, 14627, NY, USA.
| |
Collapse
|
34
|
Lyu D, Teng Y, Wang L, Wang X, Pentland A. An experimental study of tie transparency and individual perception in social networks. Proc Math Phys Eng Sci 2022. [DOI: 10.1098/rspa.2021.0744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Tie transparency, which may provide more unbiased information to deepen mutual understanding, thus builds trust and prompts cooperation in social networks. Little is known, however, about social connections’ transparency. We introduce knowable degree (KD) to characterize the transparency of a social tie, defined as the number of other entities who perceive the tie. We design a two-phase experiment to collect KDs in a network of 155 students. We find that structural property and node attribute significantly predict tie transparency. Meanwhile, we also find there almost always exist a few covert ties due to non-reciprocity. Furthermore, we focus on exploring the boundary of scopes of perception and evaluating individuals’ perceptual capability. We describe the two degrees of perception phenomenon that people can generally catch the relationships between their 2-neighbours at most. We propose a generic quantitative model to recognize high-capability perceivers, who are found more sociable and enjoy exploring the social context as well.
Collapse
Affiliation(s)
- Ding Lyu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
| | - Yansong Teng
- Baidu Incorporated, Beijing, People’s Republic of China
| | - Lin Wang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
| | - Xiaofan Wang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
- Department of Automation, Shanghai University, Shanghai, People’s Republic of China
| | - Alex Pentland
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
35
|
Anwar MS, Rakshit S, Ghosh D, Bollt EM. Stability analysis of intralayer synchronization in time-varying multilayer networks with generic coupling functions. Phys Rev E 2022; 105:024303. [PMID: 35291066 DOI: 10.1103/physreve.105.024303] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
The stability analysis of synchronization patterns on generalized network structures is of immense importance nowadays. In this article, we scrutinize the stability of intralayer synchronous state in temporal multilayer hypernetworks, where each dynamic units in a layer communicate with others through various independent time-varying connection mechanisms. Here, dynamical units within and between layers may be interconnected through arbitrary generic coupling functions. We show that intralayer synchronous state exists as an invariant solution. Using fast-switching stability criteria, we derive the condition for stable coherent state in terms of associated time-averaged network structure, and in some instances we are able to separate the transverse subspace optimally. Using simultaneous block diagonalization of coupling matrices, we derive the synchronization stability condition without considering time-averaged network structure. Finally, we verify our analytically derived results through a series of numerical simulations on synthetic and real-world neuronal networked systems.
Collapse
Affiliation(s)
- Md Sayeed Anwar
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - Sarbendu Rakshit
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - Dibakar Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - Erik M Bollt
- Department of Mathematics, Department of Electrical and Computer Engineering, Department of Physics, Clarkson University, Potsdam, New York 13699, USA
| |
Collapse
|
36
|
Shridhar SV, Alexander M, Christakis NA. Characterizing super-spreaders using population-level weighted social networks in rural communities. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210123. [PMID: 34802276 DOI: 10.1098/rsta.2021.0123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 07/07/2021] [Indexed: 05/22/2023]
Abstract
Sociocentric network maps of entire populations, when combined with data on the nature of constituent dyadic relationships, offer the dual promise of advancing understanding of the relevance of networks for disease transmission and of improving epidemic forecasts. Here, using detailed sociocentric data collected over 4 years in a population of 24 702 people in 176 villages in Honduras, along with diarrhoeal and respiratory disease prevalence, we create a social-network-powered transmission model and identify super-spreading nodes as well as the nodes most vulnerable to infection, using agent-based Monte Carlo network simulations. We predict the extent of outbreaks for communicable diseases based on detailed social interaction patterns. Evidence from three waves of population-level surveys of diarrhoeal and respiratory illness indicates a meaningful positive correlation with the computed super-spreading capability and relative vulnerability of individual nodes. Previous research has identified super-spreaders through retrospective contact tracing or simulated networks. By contrast, our simulations predict that a node's super-spreading capability and its vulnerability in real communities are significantly affected by their connections, the nature of the interaction across these connections, individual characteristics (e.g. age and sex) that affect a person's ability to disperse a pathogen, and also the intrinsic characteristics of the pathogen (e.g. infectious period and latency). This article is part of the theme issue 'Data science approach to infectious disease surveillance'.
Collapse
Affiliation(s)
- Shivkumar Vishnempet Shridhar
- School of Engineering and Applied Science, Yale University, 17 Hillhouse Ave, New Haven, CT 06520, USA
- Yale Institute for Network Science, Yale University, 17 Hillhouse Ave, New Haven, CT 06520, USA
| | - Marcus Alexander
- Yale Institute for Network Science, Yale University, 17 Hillhouse Ave, New Haven, CT 06520, USA
| | - Nicholas A Christakis
- Yale Institute for Network Science, Yale University, 17 Hillhouse Ave, New Haven, CT 06520, USA
| |
Collapse
|
37
|
McMillan C. Worth the Weight: Conceptualizing and Measuring Strong Versus Weak Tie Homophily. SOCIAL NETWORKS 2022; 68:139-147. [PMID: 34305296 PMCID: PMC8294076 DOI: 10.1016/j.socnet.2021.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Homophily, or the tendency for social contact to occur among those who are similar, plays a crucial role in structuring our social networks. Most previous work considers whether homophily shapes the patterns of all social ties, regardless of their frequency of interaction or level of intimacy. As complex network data become increasingly available, however, researchers need to evaluate whether homophily operates differently for ties defined by strong versus weak measures of strength. Here, I take this approach by first defining two variants of homophily: (1) strong tie homophily, or the tendency for ties with high measures of strength to cluster together similar peers, and (2) weak tie homophily, or the tendency for ties with low edge weights to connect same-attribute actors. Then, I apply valued ERGMs to demonstrate the utility of differentiating between the two variants across simulated and observed networks. In most networks, I find that there are observable differences in the magnitude of strong versus weak tie homophily. Additionally, when there are low levels of clustering on the attribute of interest, distinguishing between strong and weak tie homophily can reveal that these processes operate in opposite directions. Since strong and weak ties carry substantively different implications, I argue that differentiating between the two homophily variants has the potential to uncover novel insights on a variety of social phenomena.
Collapse
|
38
|
Li J, Xie J, Godec A, Weninger KR, Liu C, Smith JC, Hong L. Non-ergodicity of a globular protein extending beyond its functional timescale. Chem Sci 2022; 13:9668-9677. [PMID: 36091909 PMCID: PMC9400594 DOI: 10.1039/d2sc03069a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/18/2022] [Indexed: 11/21/2022] Open
Abstract
Internal motions of folded proteins have been assumed to be ergodic, i.e., that the dynamics of a single protein molecule averaged over a very long time resembles that of an ensemble. Here, by performing single-molecule fluorescence resonance energy transfer (smFRET) experiments and molecular dynamics (MD) simulations of a multi-domain globular protein, cytoplasmic protein-tyrosine phosphatase (SHP2), we demonstrate that the functional inter-domain motion is observationally non-ergodic over the time spans 10−12 to 10−7 s and 10−1 to 102 s. The difference between observational non-ergodicity and simple non-convergence is discussed. In comparison, a single-strand DNA of similar size behaves ergodically with an energy landscape resembling a one-dimensional linear chain. The observed non-ergodicity results from the hierarchical connectivity of the high-dimensional energy landscape of the protein molecule. As the characteristic time for the protein to conduct its dephosphorylation function is ∼10 s, our findings suggest that, due to the non-ergodicity, individual, seemingly identical protein molecules can be dynamically and functionally different. Internal motions of folded proteins have been assumed to be ergodic, i.e., that the dynamics of a single protein molecule averaged over a very long time resembles that of an ensemble.![]()
Collapse
Affiliation(s)
- Jun Li
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - JingFei Xie
- Interdisciplinary Research Center on Biology and Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201203, China
- University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Aljaž Godec
- Mathematical BioPhysics Group, Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany
| | - Keith R. Weninger
- Department of Physics, North Carolina State University, Raleigh, NC 27695, USA
| | - Cong Liu
- Interdisciplinary Research Center on Biology and Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201203, China
| | - Jeremy C. Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996, USA
| | - Liang Hong
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| |
Collapse
|
39
|
Kauffman K, Werner CS, Titcomb G, Pender M, Rabezara JY, Herrera JP, Shapiro JT, Solis A, Soarimalala V, Tortosa P, Kramer R, Moody J, Mucha PJ, Nunn C. Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar. J R Soc Interface 2022; 19:20210690. [PMID: 35016555 PMCID: PMC8753172 DOI: 10.1098/rsif.2021.0690] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/16/2021] [Indexed: 11/12/2022] Open
Abstract
Social and spatial network analysis is an important approach for investigating infectious disease transmission, especially for pathogens transmitted directly between individuals or via environmental reservoirs. Given the diversity of ways to construct networks, however, it remains unclear how well networks constructed from different data types effectively capture transmission potential. We used empirical networks from a population in rural Madagascar to compare social network survey and spatial data-based networks of the same individuals. Close contact and environmental pathogen transmission pathways were modelled with the spatial data. We found that naming social partners during the surveys predicted higher close-contact rates and the proportion of environmental overlap on the spatial data-based networks. The spatial networks captured many strong and weak connections that were missed using social network surveys alone. Across networks, we found weak correlations among centrality measures (a proxy for superspreading potential). We conclude that social network surveys provide important scaffolding for understanding disease transmission pathways but miss contact-specific heterogeneities revealed by spatial data. Our analyses also highlight that the superspreading potential of individuals may vary across transmission modes. We provide detailed methods to construct networks for close-contact transmission pathogens when not all individuals simultaneously wear GPS trackers.
Collapse
Affiliation(s)
- Kayla Kauffman
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | - Courtney S. Werner
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Georgia Titcomb
- Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | | | - Jean Yves Rabezara
- Science de la Nature et Valorisation des Ressources Naturelles, Centre Universitaire Régional de la SAVA, Antalaha, Madagascar
| | | | - Julie Teresa Shapiro
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Alma Solis
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Global Health Institute, Durham, NC 27156, USA
| | | | - Pablo Tortosa
- UMR Processus Infectieux en Milieu Insulaire Tropical (PIMIT), Université de La Réunion, Ile de La Réunion, France
| | - Randall Kramer
- Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
| | - James Moody
- Department of Sociology, Duke University, Durham, NC 27708, USA
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA
| | - Charles Nunn
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Global Health Institute, Durham, NC 27156, USA
| |
Collapse
|
40
|
Studying segregation in Estonia using call data records. SOCIAL NETWORK ANALYSIS AND MINING 2021. [DOI: 10.1007/s13278-021-00817-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
41
|
Murase Y, Jo HH, Török J, Kertész J, Kaski K. Deep Learning Exploration of Agent-Based Social Network Model Parameters. Front Big Data 2021; 4:739081. [PMID: 34661097 PMCID: PMC8511694 DOI: 10.3389/fdata.2021.739081] [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: 07/10/2021] [Accepted: 09/14/2021] [Indexed: 11/17/2022] Open
Abstract
Interactions between humans give rise to complex social networks that are characterized by heterogeneous degree distribution, weight-topology relation, overlapping community structure, and dynamics of links. Understanding these characteristics of social networks is the primary goal of their research as they constitute scaffolds for various emergent social phenomena from disease spreading to political movements. An appropriate tool for studying them is agent-based modeling, in which nodes, representing individuals, make decisions about creating and deleting links, thus yielding various macroscopic behavioral patterns. Here we focus on studying a generalization of the weighted social network model, being one of the most fundamental agent-based models for describing the formation of social ties and social networks. This generalized weighted social network (GWSN) model incorporates triadic closure, homophilic interactions, and various link termination mechanisms, which have been studied separately in the previous works. Accordingly, the GWSN model has an increased number of input parameters and the model behavior gets excessively complex, making it challenging to clarify the model behavior. We have executed massive simulations with a supercomputer and used the results as the training data for deep neural networks to conduct regression analysis for predicting the properties of the generated networks from the input parameters. The obtained regression model was also used for global sensitivity analysis to identify which parameters are influential or insignificant. We believe that this methodology is applicable for a large class of complex network models, thus opening the way for more realistic quantitative agent-based modeling.
Collapse
Affiliation(s)
| | - Hang-Hyun Jo
- Department of Physics, The Catholic University of Korea, Bucheon, South Korea
| | - János Török
- Department of Theoretical Physics, Budapest University of Technology and Economics, Budapest, Hungary.,Department of Network and Data Science, Central European University, Vienna, Austria.,MTA-BME Morphodynamics Research Group, Budapest University of Technology and Economics, Budapest, Hungary
| | - János Kertész
- Department of Network and Data Science, Central European University, Vienna, Austria.,Department of Computer Science, Aalto University, Espoo, Finland
| | - Kimmo Kaski
- Department of Computer Science, Aalto University, Espoo, Finland.,The Alan Turing Institute, British Library, London, United Kingdom
| |
Collapse
|
42
|
|
43
|
Gelardi V, Le Bail D, Barrat A, Claidiere N. From temporal network data to the dynamics of social relationships. Proc Biol Sci 2021; 288:20211164. [PMID: 34583581 DOI: 10.1098/rspb.2021.1164] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. However, going from temporal network data (i.e. a stream of interactions between individuals) to a representation of the social group's evolution remains a challenge. Indeed, the temporal network at any specific time contains only the interactions taking place at that time and aggregating on successive time-windows also has important limitations. Here, we present a new framework to study the dynamic evolution of social networks based on the idea that social relationships are interdependent: as the time we can invest in social relationships is limited, reinforcing a relationship with someone is done at the expense of our relationships with others. We implement this interdependence in a parsimonious two-parameter model and apply it to several human and non-human primates' datasets to demonstrate that this model detects even small and short perturbations of the networks that cannot be detected using the standard technique of successive aggregated networks. Our model solves a long-standing problem by providing a simple and natural way to describe the dynamic evolution of social networks, with far-reaching consequences for the study of social networks and social evolution.
Collapse
Affiliation(s)
- Valeria Gelardi
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Marseille, France.,Aix Marseille Univ, CNRS, LPC, FED3C, Marseille, France
| | - Didier Le Bail
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Marseille, France
| | - Alain Barrat
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Marseille, France.,Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - Nicolas Claidiere
- Aix Marseille Univ, CNRS, LPC, FED3C, Marseille, France.,Station de Primatologie-Celphedia, CNRS UAR846, Rousset, France
| |
Collapse
|
44
|
Reisch T, Heiler G, Hurt J, Klimek P, Hanbury A, Thurner S. Behavioral gender differences are reinforced during the COVID-19 crisis. Sci Rep 2021; 11:19241. [PMID: 34584107 PMCID: PMC8478918 DOI: 10.1038/s41598-021-97394-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 08/20/2021] [Indexed: 11/08/2022] Open
Abstract
Behavioral gender differences have been found for a wide range of human activities including the way people communicate, move, provision themselves, or organize leisure activities. Using mobile phone data from 1.2 million devices in Austria (15% of the population) across the first phase of the COVID-19 crisis, we quantify gender-specific patterns of communication intensity, mobility, and circadian rhythms. We show the resilience of behavioral patterns with respect to the shock imposed by a strict nation-wide lock-down that Austria experienced in the beginning of the crisis with severe implications on public and private life. We find drastic differences in gender-specific responses during the different phases of the pandemic. After the lock-down gender differences in mobility and communication patterns increased massively, while circadian rhythms tended to synchronize. In particular, women had fewer but longer phone calls than men during the lock-down. Mobility declined massively for both genders, however, women tended to restrict their movement stronger than men. Women showed a stronger tendency to avoid shopping centers and more men frequented recreational areas. After the lock-down, males returned back to normal quicker than women; young age-cohorts return much quicker. Differences are driven by the young and adolescent population. An age stratification highlights the role of retirement on behavioral differences. We find that the length of a day of men and women is reduced by 1 h. We interpret and discuss these findings as signals for underlying social, biological and psychological gender differences when coping with crisis and taking risks.
Collapse
Affiliation(s)
- Tobias Reisch
- Section for Science of Complex Systems, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090, Vienna, Austria
- Complexity Science Hub Vienna, 1080, Vienna, Austria
| | - Georg Heiler
- Complexity Science Hub Vienna, 1080, Vienna, Austria
- Institute of Information Systems Engineering, TU Wien, 1040, Vienna, Austria
| | - Jan Hurt
- Complexity Science Hub Vienna, 1080, Vienna, Austria
| | - Peter Klimek
- Section for Science of Complex Systems, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090, Vienna, Austria
- Complexity Science Hub Vienna, 1080, Vienna, Austria
| | - Allan Hanbury
- Complexity Science Hub Vienna, 1080, Vienna, Austria
- Institute of Information Systems Engineering, TU Wien, 1040, Vienna, Austria
| | - Stefan Thurner
- Section for Science of Complex Systems, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090, Vienna, Austria.
- Complexity Science Hub Vienna, 1080, Vienna, Austria.
- Santa Fe Institute, Santa Fe, NM, 85701, USA.
| |
Collapse
|
45
|
Jialu S, Zhiqiang M, Binxin Z, Haoyang X, Fredrick Oteng A, Hu W. Collaborative Innovation Network, Knowledge Base, and Technological Innovation Performance-Thinking in Response to COVID-19. Front Psychol 2021; 12:648276. [PMID: 34557126 PMCID: PMC8452876 DOI: 10.3389/fpsyg.2021.648276] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 08/09/2021] [Indexed: 11/21/2022] Open
Abstract
Amid the pandemic of COVID-19, the collaborative innovation network of enterprises is conducive to the sharing of innovation resources, knowledge transfer, and technology diffusion, which is closely related to the improvement of corporate technological innovation performance. Based on the patent application data of listed enterprises in Jiangsu, Zhejiang, and Shanghai in China, this study constructs a cooperation matrix, describes the characteristics of collaborative innovation network from two dimensions of network structure and network relationship, introduces the breadth of the knowledge base as a moderating variable, and analyzes the nexus between characteristics of a collaborative innovation network and technological innovation performance. Based on the panel data of 193 listed companies in Jiangsu, Zhejiang, and Shanghai, this study uses a multiple linear regression model for empirical analysis. The results show a U-shaped relationship between clustering coefficient and technological innovation performance. The breadth of knowledge base strengthens the positive relationship between the structural hole and technological innovation performance. In contrast, the breadth of knowledge base weakens the positive relationship between network relationships strength and technological innovation performance. The study findings will enhance enterprises’ participation in a suitable collaborative innovation according to their knowledge-based characteristics and improve the technological innovation performance.
Collapse
Affiliation(s)
- Su Jialu
- School of Management, Jiangsu University, Zhenjiang, China
| | - Ma Zhiqiang
- School of Management, Jiangsu University, Zhenjiang, China
| | - Zhu Binxin
- School of Management, Jiangsu University, Zhenjiang, China.,Centre for Competitive Creative Design (C4D), Cranfield University, Cranfield, United Kingdom
| | - Xie Haoyang
- School of Mathematical Science, Zhejiang University, Zhejiang, China
| | | | - Weijun Hu
- School of Archaeology, Jilin University, Changchun, China
| |
Collapse
|
46
|
Ságvári B, Gulyás A, Koltai J. Attitudes towards Participation in a Passive Data Collection Experiment. SENSORS 2021; 21:s21186085. [PMID: 34577291 PMCID: PMC8473380 DOI: 10.3390/s21186085] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/03/2021] [Accepted: 09/04/2021] [Indexed: 11/23/2022]
Abstract
In this paper, we present the results of an exploratory study conducted in Hungary using a factorial design-based online survey to explore the willingness to participate in a future research project based on active and passive data collection via smartphones. Recently, the improvement of smart devices has enabled the collection of behavioural data on a previously unimaginable scale. However, the willingness to share this data is a key issue for the social sciences and often proves to be the biggest obstacle to conducting research. In this paper we use vignettes to test different (hypothetical) study settings that involve sensor data collection but differ in the organizer of the research, the purpose of the study and the type of collected data, the duration of data sharing, the number of incentives and the ability to suspend and review the collection of data. Besides the demographic profile of respondents, we also include behavioural and attitudinal variables to the models. Our results show that the content and context of the data collection significantly changes people’s willingness to participate, however their basic demographic characteristics (apart from age) and general level of trust seem to have no significant effect. This study is a first step in a larger project that involves the development of a complex smartphone-based research tool for hybrid (active and passive) data collection. The results presented in this paper help improve our experimental design to encourage participation by minimizing data sharing concerns and maximizing user participation and motivation.
Collapse
Affiliation(s)
- Bence Ságvári
- Computational Social Science—Research Center for Educational and Network Studies (CSS–RECENS), Centre for Social Sciences, Tóth Kálmán Utca 4, 1097 Budapest, Hungary; (A.G.); (J.K.)
- Institute of Communication and Sociology, Corvinus University, Fővám tér 8, 1093 Budapest, Hungary
- Correspondence:
| | - Attila Gulyás
- Computational Social Science—Research Center for Educational and Network Studies (CSS–RECENS), Centre for Social Sciences, Tóth Kálmán Utca 4, 1097 Budapest, Hungary; (A.G.); (J.K.)
| | - Júlia Koltai
- Computational Social Science—Research Center for Educational and Network Studies (CSS–RECENS), Centre for Social Sciences, Tóth Kálmán Utca 4, 1097 Budapest, Hungary; (A.G.); (J.K.)
- Department of Network and Data Science, Central European University, Quellenstraße 51, 1100 Vienna, Austria
- Faculty of Social Sciences, Eötvös Loránd University of Sciences, Pázmány Péter Sétány 1/A, 1117 Budapest, Hungary
| |
Collapse
|
47
|
Yang L, Holtz D, Jaffe S, Suri S, Sinha S, Weston J, Joyce C, Shah N, Sherman K, Hecht B, Teevan J. The effects of remote work on collaboration among information workers. Nat Hum Behav 2021; 6:43-54. [PMID: 34504299 DOI: 10.1038/s41562-021-01196-4] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 08/16/2021] [Indexed: 11/09/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic caused a rapid shift to full-time remote work for many information workers. Viewing this shift as a natural experiment in which some workers were already working remotely before the pandemic enables us to separate the effects of firm-wide remote work from other pandemic-related confounding factors. Here, we use rich data on the emails, calendars, instant messages, video/audio calls and workweek hours of 61,182 US Microsoft employees over the first six months of 2020 to estimate the causal effects of firm-wide remote work on collaboration and communication. Our results show that firm-wide remote work caused the collaboration network of workers to become more static and siloed, with fewer bridges between disparate parts. Furthermore, there was a decrease in synchronous communication and an increase in asynchronous communication. Together, these effects may make it harder for employees to acquire and share new information across the network.
Collapse
Affiliation(s)
| | - David Holtz
- Haas School of Business, University of California, Berkeley, CA, USA.,MIT Initiative on the Digital Economy, Cambridge, MA, USA
| | | | | | | | | | | | - Neha Shah
- Microsoft Corporation, Redmond, WA, USA
| | | | | | | |
Collapse
|
48
|
Flamino J, Szymanski BK, Bahulkar A, Chan K, Lizardo O. Creation, evolution, and dissolution of social groups. Sci Rep 2021; 11:17470. [PMID: 34471167 PMCID: PMC8410948 DOI: 10.1038/s41598-021-96805-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Understanding why people join, stay, or leave social groups is a central question in the social sciences, including computational social systems, while modeling these processes is a challenge in complex networks. Yet, the current empirical studies rarely focus on group dynamics for lack of data relating opinions to group membership. In the NetSense data, we find hundreds of face-to-face groups whose members make thousands of changes of memberships and opinions. We also observe two trends: opinion homogeneity grows over time, and individuals holding unpopular opinions frequently change groups. These observations and data provide us with the basis on which we model the underlying dynamics of human behavior. We formally define the utility that members gain from ingroup interactions as a function of the levels of homophily of opinions of group members with opinions of a given individual in this group. We demonstrate that so-defined utility applied to our empirical data increases after each observed change. We then introduce an analytical model and show that it accurately recreates the trends observed in the NetSense data.
Collapse
Affiliation(s)
- James Flamino
- grid.33647.350000 0001 2160 9198Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY 12180 USA ,grid.33647.350000 0001 2160 9198Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Boleslaw K. Szymanski
- grid.33647.350000 0001 2160 9198Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY 12180 USA ,grid.33647.350000 0001 2160 9198Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY 12180 USA ,grid.33647.350000 0001 2160 9198Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180 USA ,grid.432054.40000 0004 0386 2407Społeczna Akademia Nauk, Łódź, Poland
| | - Ashwin Bahulkar
- grid.33647.350000 0001 2160 9198Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY 12180 USA ,grid.33647.350000 0001 2160 9198Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Kevin Chan
- grid.420282.e0000 0001 2151 958XU.S. Army Research Laboratory, Adelphi, MD 20783 USA
| | - Omar Lizardo
- grid.19006.3e0000 0000 9632 6718Department of Sociology, University of California, Los Angeles, CA 90095 USA
| |
Collapse
|
49
|
Gozzi N, Scudeler M, Paolotti D, Baronchelli A, Perra N. Self-initiated behavioral change and disease resurgence on activity-driven networks. Phys Rev E 2021; 104:014307. [PMID: 34412322 DOI: 10.1103/physreve.104.014307] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 06/23/2021] [Indexed: 01/08/2023]
Abstract
We consider a population that experienced a first wave of infections, interrupted by strong, top-down, governmental restrictions and did not develop a significant immunity to prevent a second wave (i.e., resurgence). As restrictions are lifted, individuals adapt their social behavior to minimize the risk of infection. We explore two scenarios. In the first, individuals reduce their overall social activity towards the rest of the population. In the second scenario, they maintain normal social activity within a small community of peers (i.e., social bubble) while reducing social interactions with the rest of the population. In both cases, we investigate possible correlations between social activity and behavior change, reflecting, for example, the social dimension of certain occupations. We model these scenarios considering a susceptible-infected-recovered epidemic model unfolding on activity-driven networks. Extensive analytical and numerical results show that (i) a minority of very active individuals not changing behavior may nullify the efforts of the large majority of the population and (ii) imperfect social bubbles of normal social activity may be less effective than an overall reduction of social interactions.
Collapse
Affiliation(s)
- Nicolò Gozzi
- Networks and Urban Systems Centre, University of Greenwich, London SE10 9LS, United Kingdom
| | | | | | - Andrea Baronchelli
- City, University of London, London EC1V 0HB, United Kingdom.,The Alan Turing Institute, London NW1 2DB, United Kingdom
| | - Nicola Perra
- Networks and Urban Systems Centre, University of Greenwich, London SE10 9LS, United Kingdom
| |
Collapse
|
50
|
Wang RW, Wei SX, Ye FY. Extracting a core structure from heterogeneous information network using h-subnet and meta-path strength. J Informetr 2021. [DOI: 10.1016/j.joi.2021.101173] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
|