A particle swarm modelforstatic community detection based on homogeneous features

Community structured networks are gaining prime importance in recent researches. Membership of these networks represents some meaningful information. These networks can be further sub grouped into smaller sub-networks towards efficient management of overall network schema. In some networks these membership patterns often change and that leaves a major impact on the characteristics and behavior of the whole network. In this present work, some of those existing researches have been explored and on the basis of that study a new bio-inspired approach is proposed to identify meaningful sub-networks. This particle-swarm model (PSM) performs community detection in homogeneous network. This method has explored some essential features in community detection. Comparative experimental findings set up novelty of this present proposed research.