Many real-world social networks exist in the form of the complex network, which includes very large scale structured or unstructured data. The large scale networks like brain graph, protein structure, food web, transportation system, WorldWide Web, online social network are sparsely connected globally and densely connected locally. For detecting densely connected clusters from complex networks, graph clustering methods are useful. Graph clustering performs through partition a graph based on edge cut, vertex cut, edge betweeness, vertex similarities, topological structure of graph. Most of the graph clustering methods predominantly emphasis on topological structure of graph and not bearing in mind the vertex properties/attributes or similarity based on indirectly connected vertices. In this paper, we propose a CSGCluster, a novel collaborative similarity based graph clustering methodfor community detection ina complex network. In this, we introduce concepts, Approachable Unitto find similarities for directly connected vertices and introduced shortest path strategy for indirectly connected vertices and based on that a graph clustering method, CSG-Cluster is presented. For this, a new collaborative similarity approach is adopted to compute vertex similarities. In the CSG-Cluster method, weform a group of vertices based on distance measures based on calculated similarity with the help of K-Medoids framework. Performs experiment on two real datasets with other relevant methods in whichresults shows the effectiveness of CSG-Cluster. This idea is suitable for graph database to apply collaborative similarity during query processing.
Agrawal, S. S., & Patel, A. (2019). CSG cluster: A collaborative similarity based graph clustering for community detection in complex networks. International Journal of Engineering and Advanced Technology, 8(5), 1682–1687.