Community detection based on influential nodes in dynamic networks

Abstract

Communities in a network are groups of nodes that are more strongly connected to each other. This article proposes a novel method for community detection in dynamic networks, focusing on influential nodes and overlapping communities. The method, named community detection based on adaptive multi-centrality aggregation (CDAMA), tackles two key challenges identifying influential nodes and overlapping communities. CDAMA introduces the Adaptive multi-centrality aggregation (AMCA) approach to identify influential nodes. AMCA integrates multiple centrality measures. The adaptive overlap control and merging (AOC-CM) approach addresses overlapping communities. AOC-CM utilizes structural, temporal, and semantic factors to strategically merge communities while preserving those with minimal overlap. CDAMA consists of five phases: receiving network snapshots, selecting influential nodes, launching communities, checking overlap and merging communities, and updating communities. Evaluation on three benchmark datasets demonstrates that CDAMA outperforms existing state-of-th-art methods in terms of Newman modularity, Modularity with split penalty and density modularity and Execution time. This suggests CDAMA is a valuable tool for tasks like viral marketing, information diffusion analysis, and network resilience studies.