Graph clustering based on Structural Attribute Neighborhood Similarity (SANS)

Graph Clustering techniques are widely used in detecting densely connected graphs from a graph network. Traditional Algorithms focus only on topological structure but mostly ignore heterogeneous vertex properties. In this paper we propose a novel graph clustering algorithm, SANS (Structural Attribute Neighborhood Similarity) algorithm, provides an efficient trade-off between both topological and attribute similarities. First, the algorithm partitions the graph based on structural similarity, secondly the degree of contribution of vertex attributes with the vertex in the partition is evaluated and clustered. An extensive experimental result proves the effectiveness of SANS cluster with the other conventional algorithms.