  Graph clustering is a fundamental problem that has been extensively studied
both in theory and practice. The problem has been defined in several ways in
literature and most of them have been proven to be NP-Hard. Due to their high
practical relevancy, several heuristics for graph clustering have been
introduced which constitute a central tool for coping with NP-completeness, and
are used in applications of clustering ranging from computer vision, to data
analysis, to learning. There exist many methodologies for this problem, however
most of them are global in nature and are unlikely to scale well for very large
networks. In this paper, we propose two scalable local approaches for
identifying the clusters in any network. We further extend one of these
approaches for discovering the overlapping clusters in these networks. Some
experimentation results obtained for the proposed approaches are also
presented.
