Multiobjective evolutionary community detection for dynamic networks

A multiobjective genetic algorithm for detecting communities in dynamic networks, i.e., networks that evolve over time, is proposed. The approach leverages on the concept of evolutionary clustering, assuming that abrupt changes of community structure in short time periods are not desirable. The algorithm correctly detects communities and it is shown to be very competitive w.r.t. some state-of-the-art methods. Categories and Subject Descriptors H.2.8 [Database Managment]: Database Applications — Data Mining; I.2.2 [Artificial Intelligence]: Automatic Programming; I.5.3 [Computing Methodologies]: Pattern Recognition—Clustering General Terms Algorithms. Keywords Genetic Algorithms, Data Mining, Clustering, Dynamic Networks, Community Detection.

• Type: Journal
• Year: 2010
• Where: GECCO
• Authors: Francesco Folino, Clara Pizzuti
• Keywords: Dynamic Networks, GECCO 2010, Genetic Algorithms, Multiobjective Genetic Algorithm, Optimization