Competition of simple and complex adoption on interdependent networks

Czaplicka, Agnieszka; Toral, Raúl; Miguel, M. San

doi:10.1103/physreve.94.062301

Cited by 40 publications

(38 citation statements)

References 53 publications

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“…These models were then extended to study social contagions, particularly interacting, antagonistic contagions [31][32][33], and their cascading behaviors on multiplex networks [34][35][36][37]. Shu et al [38] present the dynamics of social contagions on two interdependent two-dimensional lattices, and give examples of nodes in communication networks which are spatially embedded [39][40][41].…”

Section: Literature Reviewmentioning

confidence: 99%

Co-diffusion of social contagions

Chang

2018

New J. Phys.

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Prior social contagion models consider the spread of either one contagion on interdependent networks or multiple contagions on single layer networks, usually under assumptions of competition. We propose a new threshold model for the diffusion of multiple contagions. Individuals are placed on a multiplex network with a periodic lattice layer and a random-regular-graph layer. On these population structures, we study the interface between two key aspects of the diffusion process: the level of synergy between two contagions, and the rate at which individuals become dormant after adoption. Dormancy is defined as a looser form of immunity that limits active spreading but without conferring resistance. Monte Carlo simulations reveal lower synergy makes contagions more susceptible to percolation, especially those that diffuse on lattices. Faster diffusion of one contagion with dormancy probabilistically blocks the diffusion of the other, in a way similar to ring vaccination. We show that within a band of synergy, bimodal or trimodal branchings occur on the slower contagion on the lattice. We also show complimentary contagions can provide a synergistic boost to help spread contagions that have almost gone dormant.

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Section: Literature Reviewmentioning

confidence: 99%

Co-diffusion of social contagions

Chang

2018

New J. Phys.

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“…Out of this 85%, almost 62% are the studies where virus/disease compete with awareness/information, and over 23% constitutes literature where two viruses/diseases interact with each other. Another group, although much smaller, constitutes opinion/meme spread where we can have two memes [61] and [62] or opinion [89] spreading over the multilayer network. Other variations are where on one layer spreads opinion and on another (i) virus [91] [113], (ii) decision making process [115], or (iii) adoption of green behaviour [114].…”

Section: A What Spreads?mentioning

confidence: 99%

“…In [89], authors propose to have M interlayer edges between two layers that randomly connect nodes between two layers. Thus, the change in the node opinion is affected both by the neighbours in its layer and by the neighbours in the other layer.…”

Section: ) Edges Between Layersmentioning

confidence: 99%

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

2020

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The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward.INDEX TERMS complex networks, information diffusion, multilayer networks, spreading processes

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“…On the other hand, they note URL sharing is competitive. The focus of theoretical models has been diverse, ranging from a pair of simple and complex layers [25], to trusted and distrusted edges between layers [26]. These studies focus on competing contagions, though recently there has been directed research effort toward synergy.…”

Section: Introductionmentioning

confidence: 99%

Co-contagion diffusion on multilayer networks

Chang

2019

Appl Netw Sci

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This study examines the interface of three elements during co-contagion diffusion: the synergy between contagions, the dormancy rate of each individual contagion, and the multiplex network topology. Dormancy is defined as a weaker form of "immunity," where dormant nodes no longer actively participate in diffusion, but are still susceptible to infection. The proposed model extends the literature on threshold models, and demonstrates intricate interdependencies between different graph structures. Our simulations show that first, the faster contagion induces branching on the slower contagion; second, shorter characteristic path lengths diminish the impact of dormancy in lowering diffusion. Third, when two long-range graphs are paired, the faster contagion depends on both dormancy rates, whereas the slower contagion depends only on its own; fourth, synergistic contagions are less sensitive to dormancy, and have a wider window to diffuse. Furthermore, when long-range and spatially constrained graphs are paired, ring vaccination occurs on the spatial graph and produces partial diffusion, due to dormant, surrounding nodes. The spatial contagion depends on both dormancy rates whereas the long-range contagion depends on only its own.

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Competition of simple and complex adoption on interdependent networks

Cited by 40 publications

References 53 publications

Co-diffusion of social contagions

Co-diffusion of social contagions

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

Co-contagion diffusion on multilayer networks

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