Graph Clustering with Density-Cut

TL;DR

This paper introduces Dcut, a novel graph clustering algorithm based on density connectivity, which effectively identifies meaningful communities by constructing a density-connected tree and evaluating cluster quality naturally.

Contribution

The paper presents Dcut, a new density-based graph clustering method that constructs a density-connected tree for efficient and meaningful community detection.

Findings

Dcut effectively identifies dense clusters in synthetic and real networks.

The method provides an intuitive criterion for evaluating clustering quality.

Dcut offers efficient partitioning based on local density connectivity.

Abstract

How can we find a good graph clustering of a real-world network, that allows insight into its underlying structure and also potential functions? In this paper, we introduce a new graph clustering algorithm Dcut from a density point of view. The basic idea is to envision the graph clustering as a density-cut problem, such that the vertices in the same cluster are densely connected and the vertices between clusters are sparsely connected. To identify meaningful clusters (communities) in a graph, a density-connected tree is first constructed in a local fashion. Owing to the density-connected tree, Dcut allows partitioning a graph into multiple densely tight-knit clusters directly. We demonstrate that our method has several attractive benefits: (a) Dcut provides an intuitive criterion to evaluate the goodness of a graph clustering in a more natural and precise way; (b) Built upon the density-connected tree, Dcut allows identifying the meaningful graph clusters of densely connected vertices efficiently; (c) The density-connected tree provides a connectivity map of vertices in a graph from a local density perspective. We systematically evaluate our new clustering approach on synthetic as well as real data to demonstrate its good performance.