InOvIn: A fuzzy-rough approach for detecting overlapping communities with intrinsic structures in evolving networks

Real-world networks, such as biological, biomedical and social networks, often contain overlapping and intrinsic communities. More significantly, such networks are growing or evolving over time, which leads to a continuous alteration of community structures. Detecting overlapping community together with intrinsic structures in evolving scenarios is one of the challenging tasks. Prior researches are limited in handling all the events together while designing a community detector. We propose an integrated solution, InOvIn (Intrinsic Overlapping Community Detection in Incremental Networks), for detecting overlapping, non-overlapping and intrinsic communities in evolving networks. Herein, we have explored a rough-fuzzy clustering approach for overlapping community detection. Fuzzy membership helps in soft decision making for deciding membership of a node towards a target community. While rough boundary of the communities decides the shared membership of a node in multiple communities. The node degree density variation measure is used to discover the existence of intrinsic community within a community. We assess the performance of InOvIn in light of twelve (12) popular real-world social networks. It may be noted that available real-world networks are lacking in labeled overlapping and intrinsic communities. Hence, we synthetically generate six (O6) networks with both overlapping and intrinsic communities. We demonstrate the superiority of InOvIn over contemporary community detection methods using ten (1O) different statistical assessment parameters. Interestingly, for the first time, our method detects intrinsic communities in PolBooks and Word Adjacencies networks. (C) 2020 Elsevier B.V. All rights reserved.