Analysis of Graph Clustering Method

Abstract

Network data clustering has vital importance in various domains such as social network analysis, epidemiology, World Wide Web analysis, etc. The clustering technique derives underlying structures present in the graph. Along with the cluster creation, vertices classification is also an important task. To detect hubs and outlier is very important task in graph mining. There are various graph clustering algorithm such as graph partitioning, density based, modularity based, etc. This work aims to study various techniques of graph data clustering