Social contagions on interdependent lattice networks

Abstract

Although an increasing amount of research is being done on the dynamical processes on interdependent spatial networks, knowledge of how interdependent spatial networks influence the dynamics of social contagion in them is sparse. Here we present a novel non-Markovian social contagion model on interdependent spatial networks composed of two identical two-dimensional lattices. We compare the dynamics of social contagion on networks with different fractions of dependency links and find that the density of final recovered nodes increases as the number of dependency links is increased. We use a finite-size analysis method to identify the type of phase transition in the giant connected components (GCC) of the final adopted nodes and find that as we increase the fraction of dependency links, the phase transition switches from second-order to first-order. In strong interdependent spatial networks with abundant dependency links, increasing the fraction of initial adopted nodes can induce the switch from a first-order to second-order phase transition associated with social contagion dynamics. In networks with a small number of dependency links, the phase transition remains second-order. In addition, both the second-order and first-order phase transition points can be decreased by increasing the fraction of dependency links or the number of initially-adopted nodes.

Complex Contagions: A Decade in Review

“…The first direction investigates how complex contagions spread within large networks of varying topologies. To date, researchers have examined threshold-based contagion models within power-law , locally tree-like (Gleeson and Cahalane 2007), degree-correlated (Gleeson 2008;Dodds and Payne 2009), directed (Gai and Kapadia 2010), weighted (Hurd and Gleeson 2013), small-world ), modular (Galstyan and Cohen 2007), clustered (Ikeda et al 2010;Hackett et al 2011), temporal (Karimi et al 2013;Backlund et al 2014), multiplex (Brummitt et al 2012;Yağan et al 2012, Lee et al 2014, and interdependent lattice networks (Shu et al 2017). Researchers have used different topologies to simulate how external factors like mass media influence cascade dynamics (Bassett et al 2012), and how topologies influence percolation processes (Zhao et al 2013).…”

Co-diffusion of social contagions

“…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]. Li et al [42] use a similar set-up to study the spread of epidemics, where they first pair two interconnected lattices, then pair two Erdős-Rényi (ER) networks.…”

Co-contagion diffusion on multilayer networks

“…In particular, research has shown the importance of topological features, such as collective influence by degree [20], stochasticity and noise [21], and strategy/topology coevolution [22]. Shu et al [23] study contagions on two interdependent lattices, which are spatially constrained.…”