Deep community detection based on memetic algorithm

Shanfeng Wang, Maoguo Gong, Bo Shen, Zhao Wang, Qing Cai, Licheng Jiao

Abstract: Deep community can be detected by removing noise nodes or edges from a network. A centrality measure, named local Fiedler vector centrality is proposed for deep community detection. Algorithms to optimize local Fiedler vector centrality are either with high computation complexity or difficult to find the optimal solution of local Fiedler vector centrality. In this paper, a novel memetic algorithm is proposed to maximize local Fiedler vector centrality for deep community detection. Experiments of our proposed memetic algorithm on four real world networks demonstrate that our algorithm can find optimal solution of local Fiedler vector centrality and is effective to discover deep communities.