A discrete Krill herd optimization algorithm for community detection

The rapid increase on the social networks presents an urgent need for identifying the community detection. Community detection is process of defining complex networks topology or structure using an objective quality function by clustering or grouping these complex networks as sets or groups of nodes and edges based on their connectivity. This paper presents a discrete Krill herd swarm optimization algorithm for community detection problem (AKHSO) as an efficient optimization technique to handle the problem of complex networks community detection. AKHSO is able to define dynamically the number of communities in the process. A comparison is conducted with well-known community quality measures and benchmarks. The experiment is executed on real life popular benchmarks data sets. The experiment proved that AKHSO can handle the community detection problem and define the structure of complex networks with high accuracy and quality.