Precise Community Discovery in Attributed Graph Using Semantic and Structural Characteristics

Abstract

Community identification is one of the most well-known and emerging research areasin social network analysis. Most of the network community discovery methods are developed with structure cohesiveness and keyword cohesiveness but given lees importance to the semantic information of communities.This studyproposes a semantics-based precise community detection mechanism from large attributed graphs. A group of relevant nodes that have a logical correlation with the query is extracted in work. The publically available DBLP datasetis used in the experiments to validate our method. We could successfully integratethe structure and semantic information of the graphs toeffectively detect the community as per the keywords and size of the community. The results obtained in the experiments were comparable and promising for the community working in the domain.