A novel relevance-based information interaction model for community detection in complex networks

Identification of patronized communities in a complex network structure is an essential aspect of network analysis. These identifications really become challenging when we come across the tasks of dealing with such large-scale community networks when their vertices and edges span over billions of entities like social media networks, news networks, welfare associations, NGO networks etc., to look for a pattern match. Although several incentive approaches have been developed and deployed to ease the process, still there is no valuable alleviation in the severity of the concern; for instance, some of the methods just consider the local information of a node in a network, whereas some of them consider only global information, and some more methods need end-users to provide advanced knowledge about the community structure. To cope with these research issues, an effective community detection approach is required to feasibly alleviate the computational overhead of dealing with the network. Therefore, we propose a novel Relevance-based Information Interaction Model (RIIM) to identify communities in complex networks based on local as well as global topological aspects without providing prior community knowledge and parameter configuration. In order to evaluate the effectiveness of RIIM, we conducted extensive experiments on real and synthetic networks, and the generated performance demonstrates that the proposed approach outperformed the state-of-the-art techniques by effectively identifying the corresponding communities in complex networks.