Graph Clustering with Local Density-Cut

Junming Shao, Qinli Yang, Zhong Zhang, Jinhu Liu, Stefan Kramer

Abstract: In this paper, we introduce a new graph clustering algorithm, called Dcut. The basic idea is to envision the graph clustering as a local density-cut problem. To identify meaningful communities in a graph, a density-connected tree is first constructed in a local fashion. Building upon the local intuitive density-connected tree, Dcut allows partitioning a graph into multiple densely tight-knit clusters effectively and efficiently. We have demonstrated that our method has several attractive benefits: (a) Dcut provides an intuitive criterion to evaluate the goodness of a graph clustering in a more precise way; (b) Building upon the density-connected tree, Dcut allows identifying high-quality clusters; (c) The density-connected tree also provides a connectivity map of vertices in a graph from a local density perspective. We systematically evaluate our new clustering approach on synthetic and real-world data sets to demonstrate its good performance.