A Microblock Density-Based Similarity Measure for Graph Clustering

Graph clustering is an important technique in data mining and network analysis, and it is widely used in chemistry, physics, biology, communication, and computer science. Similarities between vertices of a graph are the fundamental conditions for many hierarchical clustering algorithms. In the paper, we propose a new similarity measure based on microblock density, which computes the similarity between a pair of vertices by calculating the densities of their common adjacent microblock. This measure extends the scope and improves the discrimination of traditional measure, thus significantly improving the performance and stability of the similarity-based clustering algorithms. Experiments on synthetic data and real networks show that the density-based similarity approach accurately reflects the local structure of the graph and provides higher accuracy similarities for clustering and community structural detection algorithms than other state-of-the-art methods.