A Local Approach for Identifying Clusters in Networks

TL;DR

This paper introduces two scalable local algorithms for detecting clusters in large networks, including an extension for overlapping clusters, addressing the limitations of global methods in handling big data.

Contribution

The paper presents novel local clustering algorithms that are scalable and capable of identifying overlapping clusters in large networks.

Findings

Algorithms are scalable for large networks

Effective in detecting overlapping clusters

Experimental results demonstrate practical utility

Abstract

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.