Abstract
Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.
Highlights
In last few years, many approaches have been proposed to detect communities in social networks using diverse ways
Different social networks have been taken into account for the experimental analyses conducted on the two algorithms, and for their comparison with other efficient metaheuristics which are present in literature
Two novel immunological algorithms have been developed for the community detection, one of the most challenging problems in network science, with an important impact on many research areas
Summary
Many approaches have been proposed to detect communities in social networks using diverse ways. Modeling and examining complex systems that contain biological, ecological, economic, social, technological, and other information is a very difficult process because the systems used for the real-world data representation contain highly important information, such as social relationships among people or information exchange interactions between molecular structures in a body For this reason, the study of community structures inspires intense research activities to visualize and understand the dynamics of a network at different scales [1,2,3]. Several search algorithms (both exact and approximate) for clustering problems have been proposed, and, generally, they have been proven to be robust in finding as cohesive as possible communities in large and complex networks [6,7].
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