Opinion Dynamics via a Gossip Algorithm with Asynchronous Group Interactions

抄録

Various models exist for representing the opinion formation process in social networks. The DeGroot consensus model, which is one of the earliest works in opinion dynamics, updates the opinions of the members of a group by taking into account their interrelationships and how much they value each other's opinions. In this model, the interactions and the weights given to opinions remain fixed during the entire duration of the process, which leads to a predictable behavior. As such, there are certain scenarios that can be better represented using other models. For asynchronous pairwise interactions, a gossip algorithm can be used as an alternative method for describing its opinion dynamics. This approach randomly selects the interacting pairs at every step of the algorithm. Thus, the opinion formation in this model is driven by a stochastic process. We propose to extend the pairwise gossip algorithm to accommodate asynchronous group interactions. We then analyze its behavior and compare it with the other models discussed in this paper.