Maximum Degree Based Heuristics for Influence Maximization

Abstract

Influence maximization is the problem of selecting a subset of individuals in a social network that maximizes the influence propagated in the network. With the popularity of social network sites, and the development of viral marketing, the importance of the problem has been increased. Finding the most influential vertices, called seeds, in a social network graph is an NP-hard problem, and therefore, time consuming. Many heuristics are proposed to find a nearly good solution in a shorter time. In this paper, we propose two heuristic algorithms to find a good seed set. We evaluate our algorithms on several well-known datasets and show that our heuristics achieve the best results (up to 800 improvements in influence spread) for this problem in a shorter time (up to 10% improvement in runtime).