Graph clustering using k-Neighbourhood Attribute Structural similarity

A simple and novel approach to identify the clusters based on structural and attribute similarity in graph network is proposed which is a fundamental task in community detection. We identify the dense nodes using Local Outlier Factor (LOF) approach that measures the degree of outlierness, forms a basic intuition for generating the initial core nodes for the clusters. Structural Similarity is identified using k-neighbourhood and Attribute similarity is estimated through Similarity Score among the nodes in the group of structural clusters. An objective function is defined to have quick convergence in the proposed algorithm. Through extensive experiments on dataset (DBLP) with varying sizes, we demonstrate the effectiveness and efficiency of our proposed algorithm k-Neighbourhood Attribute Structural (kNAS) over state-of-the-art methods which attempt to partition the graph based on structural and attribute similarity in field of community detection. Additionally, we find the qualitative and quantitative benefit of combining both the similarities in graph.