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Liu MX, Wang W, Liu Y, Tang M, Cai SM, Zhang HF. Social contagions on time-varying community networks. Phys Rev E 2017; 95:052306. [PMID: 28618499 DOI: 10.1103/physreve.95.052306] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2016] [Indexed: 06/07/2023]
Abstract
Time-varying community structures exist widely in real-world networks. However, previous studies on the dynamics of spreading seldom took this characteristic into account, especially those on social contagions. To study the effects of time-varying community structures on social contagions, we propose a non-Markovian social contagion model on time-varying community networks based on the activity-driven network model. A mean-field theory is developed to analyze the proposed model. Through theoretical analyses and numerical simulations, two hierarchical features of the behavior adoption processes are found. That is, when community strength is relatively large, the behavior can easily spread in one of the communities, while in the other community the spreading only occurs at higher behavioral information transmission rates. Meanwhile, in spatial-temporal evolution processes, hierarchical orders are observed for the behavior adoption. Moreover, under different information transmission rates, three distinctive patterns are demonstrated in the change of the whole network's final adoption proportion along with the growing community strength. Within a suitable range of transmission rate, an optimal community strength can be found that can maximize the final adoption proportion. Finally, compared with the average activity potential, the promoting or inhibiting of social contagions is much more influenced by the number of edges generated by active nodes.
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Affiliation(s)
- Mian-Xin Liu
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Wei Wang
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Ying Liu
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
- School of Computer Science, Southwest Petroleum University, Chengdu 610500, People's Republic of China
| | - Ming Tang
- School of Information Science Technology, East China Normal University, Shanghai 200241, People's Republic of China
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Shi-Min Cai
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Hai-Feng Zhang
- School of Mathematical Science, Anhui University, Hefei 230601, People's Republic of China
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102
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Liu QH, Wang W, Tang M, Zhou T, Lai YC. Explosive spreading on complex networks: The role of synergy. Phys Rev E 2017; 95:042320. [PMID: 28505757 DOI: 10.1103/physreve.95.042320] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Indexed: 11/07/2022]
Abstract
In spite of the vast literature on spreading dynamics on complex networks, the role of local synergy, i.e., the interaction of elements that when combined produce a total effect greater than the sum of the individual elements, has been studied but only for irreversible spreading dynamics. Reversible spreading dynamics are ubiquitous but their interplay with synergy has remained unknown. To fill this knowledge gap, we articulate a model to incorporate local synergistic effect into the classical susceptible-infected-susceptible process, in which the probability for a susceptible node to become infected through an infected neighbor is enhanced when the neighborhood of the latter contains a number of infected nodes. We derive master equations incorporating the synergistic effect, with predictions that agree well with the numerical results. A striking finding is that when a parameter characterizing the strength of the synergy reinforcement effect is above a critical value, the steady-state density of the infected nodes versus the basic transmission rate exhibits an explosively increasing behavior and a hysteresis loop emerges. In fact, increasing the synergy strength can promote the spreading and reduce the invasion and persistence thresholds of the hysteresis loop. A physical understanding of the synergy promoting explosive spreading and the associated hysteresis behavior can be obtained through a mean-field analysis.
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Affiliation(s)
- Quan-Hui Liu
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Wang
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ming Tang
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tao Zhou
- Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.,Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ying-Cheng Lai
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA
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