Learning information diffusion model for extracting influential nodes in a social network

Abstract

We address the problem of ranking influential nodes in complex social networks by estimating diffusion probabilities from observed information diffusion data using the popular independent cascade (IC) model. For this purpose we formulate the likelihood for information diffusion data which is a set of time sequence data of active nodes and propose an iterative method to search for the probabilities that maximizes this likelihood. We apply this to two real world social networks in the simplest setting where the probability is uniform for all the links, and show that when there is a reasonable amount of information diffusion data, the accuracy of the probability is outstandingly good, and the proposed method can predict the high ranked influential nodes much more accurately than the well studied conventional four heuristic methods.