Information cascades in social networks via dynamic system analyses

Systematically analyzing the dynamic behaviors of social networks is one of the central topic in understanding the structure of large networks. In particular, the information cascade [1] introduced by Banerjee provides great insights in characterizing the opinion exchanging between network agents. Traditionally studies of information cascades focus on the Bayesian models, which are often difficult to model real world situations. In this paper, we attempt to study the information cascades from a non-Bayesian point of view. In particular, we consider a sequential decision model but with an arbitrary decision rule. We show that the fraction of agents in a network making any specific decision will converge. Thus, the agents in the network reach a sort of consensus with high probability, which allows us to predict the herd behaviors. In addition, we also apply our non-Bayesian model to different network structures, such as ER model and network with communities, in which the affect of information cascades are quantified. Finally, we simulate the decision process for multiple communities, which justifies our proposed model to comprehend real world complex user behaviors and dynamics.