A new community-based algorithm based on a “peak-slope-valley” structure for influence maximization on social networks

Influence Maximization (IM) is a key algorithmic problem that has been extensively studied in social influence analysis, but most of existing researches either make sacrifices in solution accuracy or suffer high computational complexity. In this paper, we propose a new Community-based Influence Maximization (CIM) algorithm for identifying a set of seed spreaders in a social network to maximize the expected number of influenced nodes. In CIM, the initial candidate seeds are first selected based on the proposed topological potential “peak-slope-valley” structure framework. Then, we propose a recursive clustering approach and a similarity indicator based on local resource allocation to partition communities. Finally, we design a community-based regional influence indicator to select seed nodes without using any prior knowledge. Experiment datasets include three artificial benchmarks with varying community strengths, as well as nine representative networks drawn from various fields. Extensive numerical simulations on both artificial and real networks indicate that (i) community-based techniques enrich the toolbox for addressing the IM problem and (ii) the derivative algorithm outperforms recent high-performing influence maximization algorithms in terms of influence propagation and coverage redundancy of the seed set with an acceptable complexity. Furthermore, our algorithm exhibits good stability on networks of varying scales and structural characteristics.