Influence Maximization Based on Community Closeness in Social Networks

The research of Influence maximization (IM) has always been a hot research topic in network analysis, which aims to find the most influential users in social networks to maximize the reach of influence. In recent year, many studies have focused on the problem of IM to improve efficiency by taking advantage of the small-scale community structures. However, the existing community-based methods only consider the number of nodes in a community and ignore the density of edge connections in a community. In addition, existing method can only be applied to non-overlapping community structures. In this paper, we propose community closeness-based influence maximization algorithm (CCIM) to select most influential nodes. CCIM considers the influence of point-to-point and point-to-community, reflecting the micro-level and meso-level influence. The experimental results on synthetic and three real datasets verify CCIM outperforms the state-of-the-art baselines.