2017/01/23 by Jonas L. Juul, Jonas Søgaard Juul, Mason A. Porter +2
Computer Science · Mathematics · Physics and Astronomy · Social Sciences · #Adaptation and Self-Organizing Systems (nlin.AO) #Combinatorics #Complex Network Analysis Techniques #Complex network #Computer science #Degree (music) #Evolutionary Game Theory and Cooperation #FOS: Computer and information sciences #FOS: Physical sciences #Graph #Mathematics #Network structure #Node (physics) #Opinion Dynamics and Social Influence #Physics #Physics and Society (physics.soc-ph) #Preferential attachment #Process (computing) #Random graph #Social and Information Networks (cs.SI) #Statistical physics #Theoretical computer science #Tree (set theory) #cs.SI #nlin.AO #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1701.06646
published in arXiv (Cornell University) (Cornell University) · 10 figures
arxiv created 2017/01/23 · openalex publication_date 2017/01/23 · arxiv updated 2017/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Network structure can have significant effects on the propagation of diseases, memes, and information on social networks. Such effects depend on the specific type of dynamical process that affects the nodes and edges of a network, and it is important to develop tractable models of spreading processes on networks to explore how network structure affects dynamics. 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 depending on the number of neighbors who have adopted that behavior. We illustrate that the synergy parameter in our model 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 local-tree approximation, we examine the spreading of synergistic memes and find good agreement on all but one of the networks on which we simulate spreading. We find for any network and for a broad family of synergistic models that one can predict which synergy-parameter values allow degree-k nodes to be activated.