Explosive spreading on complex networks: The role of synergy

Liu, Quanhui; Wang, Wei; Tang, Ming; Zhou, Tao; Lai, Ying–Cheng

doi:10.1103/physreve.95.042320

Cited by 41 publications

(29 citation statements)

References 76 publications

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“…The emergence of these new abrupt phase transitions is our main result. Abrupt phase transitions are not an odd outcome for social and biological contagion models [41][42][43][44][45][46][47][48][49][50][51][52][53], but our work introduces a new mechanism that can lead to a first-order phase transition. Notice that we are using a SISV model that exhibits only a continuous phase transition.…”

Section: Resultsmentioning

confidence: 95%

Sudden transitions in coupled opinion and epidemic dynamics with vaccination

2018

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This work consists of an epidemic model with vaccination coupled with an opinion dynamics. Our objective was to study how disease risk perception can influence opinions about vaccination and therefore the spreading of the disease. Differently from previous works we have considered continuous opinions. The epidemic spreading is governed by an SIS-like model with an extra vaccinated state. In our model individuals vaccinate with a probability proportional to their opinions. The opinions change due to peer influence in pairwise interactions. The epidemic feedback to the opinion dynamics acts as an external field increasing the vaccination probability. We performed Monte Carlo simulations in fully-connected populations. Interestingly we observed the emergence of a first-order phase transition, besides the usual activeabsorbing phase transition presented in the SIS model. Our simulations also show that with a certain combination of parameters, an increment in the initial fraction of the population that is pro-vaccine has a twofold effect: it can lead to smaller epidemic outbreaks in the short term, but it also contributes to the survival of the chain of infections in the long term. Our results also suggest that it is possible that more effective vaccines can decrease the long-term vaccine coverage. This is a counterintuitive outcome, but it is in line with empirical observations that vaccines can become a victim of their own success.

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Section: Resultsmentioning

confidence: 95%

Sudden transitions in coupled opinion and epidemic dynamics with vaccination

2018

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“…• In edge-centric approaches, one still considers the transmission channel from infected nodei to susceptible nodej. Now, however, the transmission rate w  i j is affected by the neighborhoods ofi and/orj (for specific examples, see [28,29,44]). Considering only nearest-neighbors, the transmission rate from nodei to nodej at timet, w  ( | ( ) ( )) t z t z t ,…”

Section: Appendix a Non-markovian Gillespie Algorithmmentioning

confidence: 99%

Memory-induced complex contagion in epidemic spreading

Hoffmann

Boguñá

2019

New J. Phys.

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Albeit epidemic models have evolved into powerful predictive tools for the spread of diseases and opinions, most assume memoryless agents and independent transmission channels. We develop an infection mechanism that is endowed with memory of past exposures and simultaneously incorporates the joint effect of multiple infectious sources. Analytic equations and simulations of the susceptible-infected-susceptible model in unstructured substrates reveal the emergence of an additional phase that separates the usual healthy and endemic ones. This intermediate phase shows fundamentally distinct characteristics, and the system exhibits either excitability or an exotic variant of bistability. Moreover, the transition to endemicity presents hybrid aspects. These features are the product of an intricate balance between two memory modes and indicate that non-Markovian effects significantly alter the properties of spreading processes.

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“…Finally, a recent paper [38] explored a variant of d-synergy. They used an SIS model to study the effect of d-synergy in networks.…”

Section: Modeling Synergy In Spreading Processesmentioning

confidence: 99%

“…Reference [6] considered the effect of edge rewiring and nearest-neighbor synergy (so-called "r-synergy") on the invasiveness of diseases in various 2D lattices. Finally, a very recent paper [38] explored a model for reversible synergistic spreading. The model was based on the susceptible-infectious-susceptible (SIS) model rather than the SIR model, and infectious nodes become susceptible again at some rate µ.…”

Section: Introductionmentioning

confidence: 99%

Synergistic effects in threshold models on networks

Juul

Porter

2018

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can-depending on a parameter-either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

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Explosive spreading on complex networks: The role of synergy

Cited by 41 publications

References 76 publications

Sudden transitions in coupled opinion and epidemic dynamics with vaccination

Sudden transitions in coupled opinion and epidemic dynamics with vaccination

Memory-induced complex contagion in epidemic spreading

Synergistic effects in threshold models on networks

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