A Multi-objective Optimization Approach for Influence Maximization in Social Networks

Abstract

Influence maximization (IM) is to select a set of seed nodes in a social network that maximizes the influence spread. The scalability of IM is a key factor in large scale online social networks. Most of existing approaches, such as greedy approaches and heuristic approaches, are not scalable or don’t provide consistently good performance on influence spreads. In this paper, we propose a multi-objective optimization method for IM problem. The IM problem is formulated to a multi-objective problem (MOP) model including two optimization objectives, i.e., spread of influence and cost. Furthermore, we develop a multi-objective differential evolution algorithm to solve the MOP model of the IM problem. Finally, we evaluate the proposed method on a real-world dataset. The experimental results show that the proposed method has a good performance in terms of effectiveness.