Clustering of networks using the fish school search algorithm

A network is an aggregation of nodes joined by edges, representing entities and their relationships. In social network clustering, nodes are organized into clusters according to their connectivity patterns, with the goal of community detection. The detection of community structures in networks is essential. However, existing techniques for community detection have not yet utilized the potential of the Fish School Search (FSS) algorithm and modularity principles. We have proposed a novel method, clustering with the Fish School Search algorithm and modularity function (FSC), that enhances modularity in network clustering by iteratively partitioning the network and optimizing the modularity function using the Fish School Search Algorithm. This approach facilitates the discovery of highly modular community structures, improving the resolution and effectiveness of network clustering. We tested FSC on well-known and unknown network structures. Also, we tested it on a network generated using the LFR model to test its performance on networks with different community structures. Our methodology demonstrates strong performance in identifying community structures, indicating its effectiveness in capturing cohesive communities and accurately identifying actual community structures.