Learning information diffusion model in a social network for predicting influence of nodes

Abstract

We address the problem of estimating the parameters, from observed data in a complex social network, for an information diffusion model that takes time-delay into account, based on the popular independent cascade (IC) model. For this purpose we formulate the likelihood to obtain the observed data which is a set of time-sequence data of infected (active) nodes, and propose an iterative method to search for the parameters (time-delay and diffusion) that maximize this likelihood. We first show by using a synthetic network that the proposed method outperforms the similar existing method. Next, we apply this method to problems of both 1) predicting the influence of nodes for the considered information diffusion model and 2) ranking the influential nodes. Using three large social networks, we demonstrate the effectiveness of the proposed method.