Dynamics of social contagions with heterogeneous adoption thresholds: crossover phenomena in phase transition

Wang, Wei; Tang, Ming; Shu, Panpan; Wang, Zhen

doi:10.1088/1367-2630/18/1/013029

Cited by 88 publications

(62 citation statements)

References 56 publications

(115 reference statements)

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“…Ref. [21]. In our result, there is a clear asymmetric steady state in the negative or mildly positive couplings, on one hand, and positive coupling on the other.…”

Section: Discussionsupporting

confidence: 63%

Social contagion with degree-dependent thresholds

Lee

Holme

2017

Phys. Rev. E

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We investigate opinion spreading by a threshold model in a situation where the influence of people is heterogeneously distributed. We focus on the response of the average opinion as a function between the trend between out-degree (number of neighbors)-effectively the strength of influence of a node-and the threshold for adopting a new product or opinion. We find that if the coupling is very positive, the final state of the system will be a mix of different opinions otherwise it will it converges to a consensus state. We find that this cannot be simply explained as a phase transition, but emerges from a combination of mechanisms and their relative dominance in different regions of parameter space.

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“…Ref. [21]. In our result, there is a clear asymmetric steady state in the negative or mildly positive couplings, on one hand, and positive coupling on the other.…”

Section: Discussionsupporting

confidence: 63%

Social contagion with degree-dependent thresholds

Lee

Holme

2017

Phys. Rev. E

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“…With respect to coupled disease-behavior dynamics on multilayer networks, one recent work firstly provided a brief, conceptual framework, where the system is composed of two networks: the social contagion network [610,611] and the biological contagion network [609]. The former is assumed to support the spreading of opinions and sentiments related with vaccination, while the latter allows the propagation of the virus Figure 39: Outbreak probability with (grey) and without (black) opinion formation in dependence on the vaccination coverage.…”

Section: The General Framework and An Examplementioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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“…To realistically describe the dynamics of social contagion, we propose a non-Markovian susceptible-exposedadopted-recovered spreading threshold model inspired by [31][32][33]. At each time step individuals are in either a susceptible, exposed, adopted, or recovered state.…”

Section: Model Descriptionmentioning

confidence: 99%

Effects of time-delays in the dynamics of social contagions

2018

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Time-delays are pervasive in such real-world complex networks as social contagions and biological systems, and they radically alter the evolution of the dynamic processes in networks. We use a non-Markovian spreading threshold model to study the effects of time-delays on social contagions. Using extensive numerical simulations and theoretical analyses we find that relatively long time-delays induce a microtransition in the evolution of a fraction of recovered individuals, i.e., the fraction of recovered individuals versus time exhibits multiple phase transitions. The microtransition is sharper and more obvious when high-degree individuals have a higher probability of experiencing timedelays, and the microtransition is obscure when the time-delay distribution reaches heterogeneity. We use an edge-based compartmental theory to analyze our research and find that the theoretical results agree well with our numerical simulation results.

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Dynamics of social contagions with heterogeneous adoption thresholds: crossover phenomena in phase transition

Cited by 88 publications

References 56 publications

Social contagion with degree-dependent thresholds

Social contagion with degree-dependent thresholds

Statistical physics of vaccination

Effects of time-delays in the dynamics of social contagions

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