Proximity-Aware Degree-Based Heuristics for the Influence Maximization Problem

Abstract

The problem of influence maximization is selecting the most influential individuals in a social network. With the popularity of social network sites and the development of viral marketing, the importance of the problem has increased. The influence maximization problem is NP-hard, and therefore, there will not exist any polynomial-time algorithm to solve the problem unless P = NP. Many heuristics are proposed for finding a nearly good solution in a shorter time. This study proposes two heuristic algorithms for finding good solutions. The heuristics are based on two ideas: 1) vertices of high degree have more influence in the network, and 2) nearby vertices influence on almost analogous sets of vertices. We evaluate our algorithms on several well-known data sets and show that our heuristics achieve better results (up to 15% in the influence spread) for this problem in a shorter time (up to 85% improvement in the running time).

Keywords

• degree centrality
• heuristic algorithm
• independent cascade model
• influence maximization