1
|
Pires MA, Oestereich AL, Crokidakis N, Duarte Queirós SM. Antivax movement and epidemic spreading in the era of social networks: Nonmonotonic effects, bistability, and network segregation. Phys Rev E 2021; 104:034302. [PMID: 34654182 DOI: 10.1103/physreve.104.034302] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 08/22/2021] [Indexed: 11/07/2022]
Abstract
In this work, we address a multicoupled dynamics on complex networks with tunable structural segregation. Specifically, we work on a networked epidemic spreading under a vaccination campaign with agents in favor and against the vaccine. Our results show that such coupled dynamics exhibits a myriad of phenomena such as nonequilibrium transitions accompanied by bistability. Besides we observe the emergence of an intermediate optimal segregation level where the community structure enhances negative opinions over vaccination but counterintuitively hinders-rather than favoring-the global disease spreading. Thus our results hint vaccination campaigns should avoid policies that end up segregating excessively antivaccine groups so that they effectively work as echo chambers in which individuals look to confirmation without jeopardizing the safety of the whole population.
Collapse
Affiliation(s)
- Marcelo A Pires
- Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro/RJ, Brazil
| | | | - Nuno Crokidakis
- Instituto de Física, Universidade Federal Fluminense, Niterói/RJ, Brazil
| | - Sílvio M Duarte Queirós
- Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro/RJ, Brazil.,National Institute of Science and Technology for Complex Systems, Rio de Janeiro/RJ, Brazil.,i3N, Campus de Santiago, Universidade de Aveiro, 3810-193 Aveiro, Portugal
| |
Collapse
|
2
|
Pei S. Influencer identification in dynamical complex systems. JOURNAL OF COMPLEX NETWORKS 2020; 8:cnz029. [PMID: 32774857 PMCID: PMC7391989 DOI: 10.1093/comnet/cnz029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 07/13/2019] [Indexed: 06/11/2023]
Abstract
The integrity and functionality of many real-world complex systems hinge on a small set of pivotal nodes, or influencers. In different contexts, these influencers are defined as either structurally important nodes that maintain the connectivity of networks, or dynamically crucial units that can disproportionately impact certain dynamical processes. In practice, identification of the optimal set of influencers in a given system has profound implications in a variety of disciplines. In this review, we survey recent advances in the study of influencer identification developed from different perspectives, and present state-of-the-art solutions designed for different objectives. In particular, we first discuss the problem of finding the minimal number of nodes whose removal would breakdown the network (i.e. the optimal percolation or network dismantle problem), and then survey methods to locate the essential nodes that are capable of shaping global dynamics with either continuous (e.g. independent cascading models) or discontinuous phase transitions (e.g. threshold models). We conclude the review with a summary and an outlook.
Collapse
Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, USA
| |
Collapse
|
3
|
Wang W, Liu QH, Liang J, Hu Y, Zhou T. Coevolution spreading in complex networks. PHYSICS REPORTS 2019; 820:1-51. [PMID: 32308252 PMCID: PMC7154519 DOI: 10.1016/j.physrep.2019.07.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 06/27/2019] [Accepted: 07/18/2019] [Indexed: 05/03/2023]
Abstract
The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.
Collapse
Affiliation(s)
- Wei Wang
- Cybersecurity Research Institute, Sichuan University, Chengdu 610065, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Quan-Hui Liu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Compleχ Lab, University of Electronic Science and Technology of China, Chengdu 610054, China
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Junhao Liang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Yanqing Hu
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
- Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, 519082, China
| | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Compleχ Lab, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
4
|
Karampourniotis PD, Szymanski BK, Korniss G. Influence Maximization for Fixed Heterogeneous Thresholds. Sci Rep 2019; 9:5573. [PMID: 30944359 PMCID: PMC6447584 DOI: 10.1038/s41598-019-41822-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 03/19/2019] [Indexed: 11/10/2022] Open
Abstract
Influence Maximization is a NP-hard problem of selecting the optimal set of influencers in a network. Here, we propose two new approaches to influence maximization based on two very different metrics. The first metric, termed Balanced Index (BI), is fast to compute and assigns top values to two kinds of nodes: those with high resistance to adoption, and those with large out-degree. This is done by linearly combining three properties of a node: its degree, susceptibility to new opinions, and the impact its activation will have on its neighborhood. Controlling the weights between those three terms has a huge impact on performance. The second metric, termed Group Performance Index (GPI), measures performance of each node as an initiator when it is a part of randomly selected initiator set. In each such selection, the score assigned to each teammate is inversely proportional to the number of initiators causing the desired spread. These two metrics are applicable to various cascade models; here we test them on the Linear Threshold Model with fixed and known thresholds. Furthermore, we study the impact of network degree assortativity and threshold distribution on the cascade size for metrics including ours. The results demonstrate our two metrics deliver strong performance for influence maximization.
Collapse
Affiliation(s)
- P D Karampourniotis
- Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180-3590, USA. .,Social Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180-3590, USA.
| | - B K Szymanski
- Social Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180-3590, USA.,Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180-3590, USA.,Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wrocław, Poland
| | - G Korniss
- Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180-3590, USA.,Social Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180-3590, USA
| |
Collapse
|