IIMOF: An Iterative Framework to Settle Influence Maximization for Opinion Formation in Social Networks

Abstract

Influence maximization for opinion formation (IMOF) in social networks is an important problem, which is used to determine some initial nodes and propagate the most ideal opinions to the whole network. The existing researches focus on improving the opinion formation models to compute the opinion of each node. However, little work has been done to describe the IMOF process mathematically, and the current researches cannot provide an effective mechanism to deal with the IMOF. In this paper, the IMOF is formulated mathematically and solved by an iterative framework. At first, we describe the IMOF as a constrained optimization problem. Then, based on node influence and neighbor coordination, the weighted coordination model is proposed to compute the opinions of network nodes with the change of iterations. In particular, in order to determine top- $k$ influential nodes (i.e., seed nodes), an iterative framework for the IMOF, called IIMOF is presented. Based on the framework, the score and rank of each node by Iterative 2-hop algorithm, i.e., SRI2 is proposed to compute the influence score of each node. Based on small in-degree and high out-degree, one-hop measure is proposed to better reflect the rank of all initial nodes. We also prove that IIMOF converges to a stable order set within the finite iterations. The simulation results show that IIMOF has superior average opinions than the comparison algorithms.