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• W2027367074 abstract "We study the diffusion of influence in random multiplex networks where links can be of $r$ different types, and for a given content (e.g., rumor, product, political view), each link type is associated with a content dependent parameter $c_i$ in $[0,infty]$ that measures the relative bias type-$i$ links have in spreading this content. In this setting, we propose a linear threshold model of contagion where nodes switch state if their perceived proportion of active neighbors exceeds a threshold tau. Namely, a node connected to $m_i$ active neighbors and $k_i-m_i$ inactive neighbors via type-$i$ links will turn active if $sum{c_i m_i}/sum{c_i k_i}$ exceeds its threshold tau. Under this model, we obtain the condition, probability and expected size of global spreading events. Our results extend the existing work on complex contagions in several directions by i) providing solutions for coupled random networks whose vertices are neither identical nor disjoint, (ii) highlighting the effect of content on the dynamics of complex contagions, and (iii) showing that content-dependent propagation over a multiplex network leads to a subtle relation between the giant vulnerable component of the graph and the global cascade condition that is not seen in the existing models in the literature." @default.
• W2027367074 created "2016-06-24" @default.
• W2027367074 creator A5010171786 @default.
• W2027367074 creator A5064066193 @default.
• W2027367074 date "2012-09-10" @default.
• W2027367074 modified "2023-10-12" @default.
• W2027367074 title "Analysis of complex contagions in random multiplex networks" @default.
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• W2027367074 doi "https://doi.org/10.1103/physreve.86.036103" @default.
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