Community detection using Local Group Assimilation

Abstract

Clustering of vertices in complex networks to detect communities is an open challenge due to its unknown and hidden properties and broad areas. Complex networks occur in various areas and interdisciplinary fields be it biological, social, chemical, electrical or any other. Numerous methods have been proposed with significant contributions to detect communities but still its nature of properties, throw us a challenge in detecting meaningful communities in real networks. Communities are built through nodes and each node has its own significance. Similarity among nodes through their neighbors suggests common interest of those nodes. The idea is to identify small groups of similar nodes locally and gradually built communities using global information. Moreover, we are not taking any seed nodes and consider each node an important player. The identification of local structures and demarcating their boundaries possess another challenge. Here, we propose Local Group Assimilation (LGA) algorithm that identifies clusters or communities in a network graph using both local and global structure information. The algorithm compares two adjacent nodes by neighborhood similarity measure and picks the highest value pair. The highest value pairs of nodes are grouped together in such a manner that it generates initial clusters of various sizes. The local groups are merged further in iterative manner that maximizes inter cluster edge density between them. Our algorithm detects small significant and relevant communities on real networks that assimilate into larger ones with promising results. We evaluated our algorithm in both real world networks and synthetic benchmark networks and compared it with most popular state-of-the-art community detection algorithms. Our experimental results show that the LGA algorithm detects significant communities in complex networks comparable to most popular algorithms and can be used in real networks to detect communities.