Random search immune algorithm for community detection

Abstract Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important field on all those areas where complex networks represent powerful interpretation tool for describing understanding systems involved neuroscience, biology, social science, economy, many others. A challenging approach to uncover the community structure network, then revealing internal organization of nodes, Modularity optimization . In this paper, we present immune algorithm ( opt-IA ) developed detect structures, with main aim maximize modularity produced by discovered communities. order assess performance , compared overall 20 heuristics metaheuristics, among which one Hyper-Heuristic method, using biological as data set. Unlike these algorithms, entirely based fully random search process, turn combined purely stochastic operators. According obtained outcomes, shows strictly better performances than almost metaheuristics was compared; whilst turns out be comparable method. Overall, can claimed that even if driven proves reliable efficient performance. Furthermore, prove latter claim, sensitivity analysis functionality conducted, classic metrics NMI ARI NVI