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Message spreading in networks with stickiness and persistence: large clustering does not always facilitate large-scale diffusion.


ABSTRACT: Recent empirical studies have confirmed the key roles of complex contagion mechanisms such as memory, social reinforcement, and decay effects in information diffusion and behavior spreading. Inspired by this fact, we here propose a new agent-based model to capture the whole picture of the joint action of the three mechanisms in information spreading, by quantifying the complex contagion mechanisms as stickiness and persistence, and carry out extensive simulations of the model on various networks. By numerical simulations as well as theoretical analysis, we find that the stickiness of the message determines the critical dynamics of message diffusion on tree-like networks, whereas the persistence plays a decisive role on dense regular lattices. In either network, the greater persistence can effectively make the message more invasive. Of particular interest is that our research results renew our previous knowledge that messages can spread broader in networks with large clustering, which turns out to be only true when they can inform a non-zero fraction of the population in the limit of large system size.

SUBMITTER: Cui P 

PROVIDER: S-EPMC4158323 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Message spreading in networks with stickiness and persistence: large clustering does not always facilitate large-scale diffusion.

Cui Pengbi P   Tang Ming M   Wu Zhi-Xi ZX  

Scientific reports 20140909


Recent empirical studies have confirmed the key roles of complex contagion mechanisms such as memory, social reinforcement, and decay effects in information diffusion and behavior spreading. Inspired by this fact, we here propose a new agent-based model to capture the whole picture of the joint action of the three mechanisms in information spreading, by quantifying the complex contagion mechanisms as stickiness and persistence, and carry out extensive simulations of the model on various networks  ...[more]

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