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Zelenkovski K, Sandev T, Metzler R, Kocarev L, Basnarkov L. Random Walks on Networks with Centrality-Based Stochastic Resetting. ENTROPY (BASEL, SWITZERLAND) 2023; 25:293. [PMID: 36832659 PMCID: PMC9955709 DOI: 10.3390/e25020293] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/19/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
We introduce a refined way to diffusely explore complex networks with stochastic resetting where the resetting site is derived from node centrality measures. This approach differs from previous ones, since it not only allows the random walker with a certain probability to jump from the current node to a deliberately chosen resetting node, rather it enables the walker to jump to the node that can reach all other nodes faster. Following this strategy, we consider the resetting site to be the geometric center, the node that minimizes the average travel time to all the other nodes. Using the established Markov chain theory, we calculate the Global Mean First Passage Time (GMFPT) to determine the search performance of the random walk with resetting for different resetting node candidates individually. Furthermore, we compare which nodes are better resetting node sites by comparing the GMFPT for each node. We study this approach for different topologies of generic and real-life networks. We show that, for directed networks extracted for real-life relationships, this centrality focused resetting can improve the search to a greater extent than for the generated undirected networks. This resetting to the center advocated here can minimize the average travel time to all other nodes in real networks as well. We also present a relationship between the longest shortest path (the diameter), the average node degree and the GMFPT when the starting node is the center. We show that, for undirected scale-free networks, stochastic resetting is effective only for networks that are extremely sparse with tree-like structures as they have larger diameters and smaller average node degrees. For directed networks, the resetting is beneficial even for networks that have loops. The numerical results are confirmed by analytic solutions. Our study demonstrates that the proposed random walk approach with resetting based on centrality measures reduces the memoryless search time for targets in the examined network topologies.
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Affiliation(s)
- Kiril Zelenkovski
- Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2, 1000 Skopje, Macedonia
| | - Trifce Sandev
- Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2, 1000 Skopje, Macedonia
- Institute of Physics, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University, Arhimedova 3, 1000 Skopje, Macedonia
- Institute of Physics & Astronomy, University of Potsdam, D-14776 Potsdam, Germany
| | - Ralf Metzler
- Institute of Physics & Astronomy, University of Potsdam, D-14776 Potsdam, Germany
- Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea
| | - Ljupco Kocarev
- Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2, 1000 Skopje, Macedonia
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, Macedonia
| | - Lasko Basnarkov
- Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2, 1000 Skopje, Macedonia
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, Macedonia
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Public Space Layout Optimization in Affordable Housing Based on Social Network Analysis. LAND 2021. [DOI: 10.3390/land10090955] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The efficient use of public space in affordable housing is of great significance to the physical and mental health of low-income and aging residents. Previous studies have evaluated the layout and quality of public space in residential areas based on residents’ subjective satisfaction, however, there still lack studies exploring residents’ behavior patterns and the use of public spaces based on objective measurement standards. Therefore, this paper selected the public space in the large affordable housing areas in the suburbs as the research object and used social network analysis (SNA) to objectively evaluate the network density, clustering coefficient and small-world value of the public space in affordable housing from the perspective of the physical spatial network of the built public space. Based on the network structure characteristics of existing public spaces, this paper further explores the relationship between the frequency of public space use in and the characteristics of nodes’ social networks and their own attributes, and the influence of public space layout structure on the behavioral patterns of affordable housing residents. This paper puts forward proposals for the renovation and optimization of public space according to the behavioral preferences of affordable housing residents, so as to complete the network of public space, promote the interaction and communication of residents in the residential area, enhance the residents’ experience of using public space and improve the living standard of residents in the residential area.
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Zhang X, Zhang J, Yang J. Personalized recommendation algorithm in social networks based on representation learning. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-219113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Recommendation algorithm is not only widely used in entertainment media, but also plays an important role in national strategy, such as the recommendation algorithm of byte beating company. This paper studies the personalized recommendation algorithm based on representation learning. The data in social network is complex, and the data mainly exists in various platforms. This paper introduces AI (Artificial Intelligence) algorithm to guide the algorithm of representation learning, and integrates the algorithm steps of representation learning, to realize the implementation of personalized recommendation algorithm in social network, and compares the representation learning algorithm. Finally, this paper designs a method based on heat conduction and text mining to provide users with webpage recommendations and help users better mine interesting popular webpages. Research shows that the performance of IMF is better than that of PMF because it overcomes the sparsity of data by pre-filling. The accuracy of IMF is 3.69% higher than that of PMF on the epinions data set, and 6.24% higher than that of PMF on the double data set. Rtcf, socialmf, tcars, CSIT, isrec, and hesmf have better performance than PMF and IMF. Among them, rtcf, socialmf, tcars, CSIT, isrec, and hesmf improve the MAE performance of PMF by 7.6%, 6.3%, 8.8%, 7.9%, 9.5% and 14.2%, respectively.
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Affiliation(s)
- Xiaoxian Zhang
- College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China
- School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, Jilin, China
| | - Jianpei Zhang
- College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China
| | - Jing Yang
- College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China
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Iacopini I, Di Bona G, Ubaldi E, Loreto V, Latora V. Interacting Discovery Processes on Complex Networks. PHYSICAL REVIEW LETTERS 2020; 125:248301. [PMID: 33412072 DOI: 10.1103/physrevlett.125.248301] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 10/22/2020] [Accepted: 11/05/2020] [Indexed: 06/12/2023]
Abstract
Innovation is the driving force of human progress. Recent urn models reproduce well the dynamics through which the discovery of a novelty may trigger further ones, in an expanding space of opportunities, but neglect the effects of social interactions. Here we focus on the mechanisms of collective exploration, and we propose a model in which many urns, representing different explorers, are coupled through the links of a social network and exploit opportunities coming from their contacts. We study different network structures showing, both analytically and numerically, that the pace of discovery of an explorer depends on its centrality in the social network. Our model sheds light on the role that social structures play in discovery processes.
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Affiliation(s)
- Iacopo Iacopini
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
- Centre for Advanced Spatial Analysis, University College London, London W1T 4TJ, United Kingdom
- The Alan Turing Institute, The British Library, London NW1 2DB, United Kingdom
| | - Gabriele Di Bona
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
- Scuola Superiore di Catania, Università di Catania, Via Valdisavoia 9, 95123 Catania, Italy
| | - Enrico Ubaldi
- Sony Computer Science Laboratories, 6 Rue Amyot, 75005 Paris, France
| | - Vittorio Loreto
- Sony Computer Science Laboratories, 6 Rue Amyot, 75005 Paris, France
- Sapienza University of Rome, Physics Department, Piazzale Aldo Moro 5, 00185 Rome, Italy
- Complexity Science Hub Vienna, A-1080 Vienna, Austria
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
- The Alan Turing Institute, The British Library, London NW1 2DB, United Kingdom
- Complexity Science Hub Vienna, A-1080 Vienna, Austria
- Dipartimento di Fisica ed Astronomia, Università di Catania and INFN, I-95123 Catania, Italy
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Bruner E, Esteve-Altava B, Rasskin-Gutman D. A network approach to brain form, cortical topology and human evolution. Brain Struct Funct 2019; 224:2231-2245. [DOI: 10.1007/s00429-019-01900-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 05/31/2019] [Indexed: 12/13/2022]
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Knowledge Maps of Disaster Medicine in China Based on Co-Word Analysis. Disaster Med Public Health Prep 2018; 13:405-409. [PMID: 30033890 DOI: 10.1017/dmp.2018.63] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE We analyzed research themes in the field of disaster medicine in China to provide references for researchers to understand the research status and developing trends of this field. METHODS Published journal articles were retrieved. A social network analysis was conducted to visualize the relations of high-frequency key words. A cluster analysis was used to classify key words. A strategic diagram analysis was conducted to visualize clusters across the entire research field. RESULTS We retrieved 3,079 articles, from which 1,749 articles and 8,284 key words were identified after screening. High-frequency key words were classified into 6 clusters. "Medical rescue" had the highest degree and betweenness centralities. Cluster 4 was located in Quadrant I of the strategic diagrams. CONCLUSIONS "Medical rescue" is the core key word, and it serves a pivotal "bridge" function. "Emergencies" and similar terms are key words with special statuses. "Natural disaster medical rescue" and "fundamental theories of disaster medicine" constitute the primary and secondary core themes, respectively. "On-site emergency treatment techniques" is a marginalized theme. The other themes are emerging themes that offer considerable scope for future development. Generally, the scope and depth of investigations in this field should be improved. (Disaster Med Public Health Preparedness. 2019;13:405-409).
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Sarkar S, Guo R, Shakarian P. Understanding and forecasting lifecycle events in information cascades. SOCIAL NETWORK ANALYSIS AND MINING 2017. [DOI: 10.1007/s13278-017-0475-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Taylor D, Myers SA, Clauset A, Porter MA, Mucha PJ. EIGENVECTOR-BASED CENTRALITY MEASURES FOR TEMPORAL NETWORKS . MULTISCALE MODELING & SIMULATION : A SIAM INTERDISCIPLINARY JOURNAL 2017; 15:537-574. [PMID: 29046619 PMCID: PMC5643020 DOI: 10.1137/16m1066142] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centrality of each node i at each time t. We refer to this eigenvector and its components as a joint centrality, as it reflects the importances of both the node i and the time layer t. We also introduce the concepts of marginal and conditional centralities, which facilitate the study of centrality trajectories over time. We find that the strength of coupling between layers is important for determining multiscale properties of centrality, such as localization phenomena and the time scale of centrality changes. In the strong-coupling regime, we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain first-order-mover scores, which concisely describe the magnitude of nodes' centrality changes over time. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court.
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Affiliation(s)
- Dane Taylor
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599-3250, USA; and Statistical and Applied Mathematical Sciences Institute (SAMSI), Research Triangle Park, NC, 27709, USA
| | - Sean A Myers
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599-3250, USA (Current address: Department of Economics, Stanford University, Stanford, CA 94305-6072, USA)
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA; Santa Fe Institute, Santa Fe, NM 87501, USA; and BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA
| | - Mason A Porter
- Mathematical Institute, University of Oxford, OX2 6GG, UK; CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK; and Department of Mathematics, University of California, Los Angeles, CA 90095, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599-3250, USA
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Abstract
Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth’s surface; however, in modern contagions long-range edges—for example, due to airline transportation or communication media—allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct “contagion maps” that use multiple contagions on a network to map the nodes as a point cloud. By analyzing the topology, geometry, and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modeling, forecast, and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.
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Jeub LGS, Balachandran P, Porter MA, Mucha PJ, Mahoney MW. Think locally, act locally: detection of small, medium-sized, and large communities in large networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 91:012821. [PMID: 25679670 PMCID: PMC5125638 DOI: 10.1103/physreve.91.012821] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2014] [Indexed: 06/04/2023]
Abstract
It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.
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Affiliation(s)
- Lucas G S Jeub
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Prakash Balachandran
- Morgan Stanley, Montreal, Quebec, H3C 3S4, Canada and Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA
| | - Mason A Porter
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom and CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, United Kingdom
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599-3250, USA
| | - Michael W Mahoney
- International Computer Science Institute, Berkeley, California 94704, USA and Department of Statistics, University of California at Berkeley, Berkeley, California 94720, USA
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