BaCIM: Balanced Competitive Influence Maximization based on Blocked Reverse Influence Sampling

Abstract

Influence maximization, which seeks to find top influential individuals from a social network, has been extensively investigated in recent years. However, previous studies mainly focused on single diffusion or the diffusion of positive and negative messages, in which a competitor dominates the diffusion process. However, in a more realistic scenario, there is a level playing field between similar competitors. To cope with this, we introduce a new Balanced Competitive Influence Maximization (BaCIM) problem which considers the balance in information dissemination. We propose a Balanced Competitive Independent Cascade (BCIC) model to describe how two similar competitive products spread and compete in the same mobile social network. Given the competitor's seeding strategy, BaCIM aims to find a size-k seed set to maximize its own influence spread. We prove that the problem is NP-hard and the objective function is submodular, based on which a greedy algorithm is proposed with (1-1/e-ε) approximation guarantee. To handle large networks, we further propose a Blocked Reverse Influence Sampling algorithm named BRIS, in which we redesign the reverse influence sampling procedure to support the diffusion model. Experimental results on two location-based social networks and several large-scale real datasets validate effectiveness and efficiency of our algorithm.