Competitive influence maximization considering inactive nodes and community homophily

Abstract

Influence maximization has attracted much attention as a way to maximize influence spread through a social network. Most previous works focus on a non-competitive environment. But in the real world such as E-commerce, there are often multiple competitors at the same time to find influential users in order to advertise their similar products. So competitive influence maximization is introduced and some solutions are proposed. However, present studies have two limitations. First, they ignore the community homogeneity problem, which means that, information can spread easily among the individuals in the community, but it is hard to be diffused to other communities. Second, those latent users that are in inactive state are often considered to have no effects on other nodes. So most researches only consider those active nodes in terms of influence propagation. But in fact inactive nodes do have effects on the information propagation. In order to address above problems, in this paper we propose a competitive influence maximization method considering inactive nodes and community homophily. We first propose a new propagation model considering inactive nodes to simulate the information dissemination in a competitive environment, in which the inactive nodes are considered to have a weaker influence compared to the active ones. In order to break down the barriers of information dissemination between different communities, we propose a two-phase seed node selecting algorithm which has the minimum node number and maximum influence range. Our experiments on real world datasets verify the feasibility and accuracy of our proposed model and algorithm.