A Modified Degree Discount Heuristic for Influence Maximization in Social Networks

Abstract

Social networks have become an essential part of our daily lives. These networks are also becoming part of many of our financial and social decisions. Identifying the most influential users in a social network can help minimize the cost of information spread. The search for the most influential users falls within the scope of the influence maximization problem, which is proven to be NP-hard. Many studies have proposed influence maximization heuristics, such as degree and degree-discount algorithms. In this paper, we propose a modified degree-discount (MDD) heuristic and evaluate its performance against single-degree discount, degree, and random algorithms as a baseline. The heuristics are compared using a subgraph of a real Twitter dataset containing 6000 nodes. The results show that the proposed MDD algorithm outperforms the benchmarks in terms of influence maximization for different initial seed sizes.