For social networks, nodes encode social entities, such as people, twitter accounts, etc., while edges encode relationship or events between entities. Opinion dynamics model opinion evolution as dynamical processes on social networks. Traditional models of opinion dynamics consider how opinions evolve either on time-independent networks or on temporal networks with edges that follow Poisson statistics. However, in many real-life networks, interactions between individuals (and hence the edges in a network) follow non-Poisson processes, which leads to dynamics on networks with memory-dependent effects (such as stereotypes).

In this paper, we model social interactions via random walks on temporal networks and derive the opinion model that is governed by an arbitrary waiting-time distribution (WTD). When the walkers have non-Poisson interevent statistics, the corresponding opinion models yield non-Markovian dynamics naturally. We analyze the convergence to consensus of these models and illustrate a variety of induced opinion models from common WTDs (including Dirac delta, exponential, and heavy-tailed distributions). When the opinion model does not have an explicit form (such as models induced by heavy-tailed WTDs), we provide a discrete-time approximation method and derive an associate set of discrete-time opinion-dynamics models.

Zoom link: https://ncsu.zoom.us/j/97638681103?pwd=dDJrRkE3d3NQZEhrRlhOMDc4T0pRUT09