Weighted Graph Clustering via Scale Contraction and Graph Structure Learning

TL;DR

This paper introduces a novel graph clustering method that reduces graph size and mitigates noise from edge weights, leading to improved clustering accuracy and efficiency on real-world datasets.

Contribution

The paper proposes a contractile graph clustering network that jointly optimizes clustering and edge weight refinement, addressing noise and scalability issues.

Findings

Outperforms baseline methods in clustering accuracy.

Reduces training time and storage space significantly.

Effectively mitigates noisy edge impacts during clustering.

Abstract

Graph clustering aims to partition nodes into distinct clusters based on their similarity, thereby revealing relationships among nodes. Nevertheless, most existing methods do not fully utilize these edge weights. Leveraging edge weights in graph clustering tasks faces two critical challenges. (1) The introduction of edge weights may significantly increase storage space and training time, making it essential to reduce the graph scale while preserving nodes that are beneficial for the clustering task. (2) Edge weight information may inherently contain noise that negatively impacts clustering results. However, few studies can jointly optimize clustering and edge weights, which is crucial for mitigating the negative impact of noisy edges on clustering task. To address these challenges, we propose a contractile edge-weight-aware graph clustering network. Specifically, a cluster-oriented graph contraction module is designed to reduce the graph scale while preserving important nodes. An edge-weight-aware attention network is designed to identify and weaken noisy connections. In this way, we can more easily identify and mitigate the impact of noisy edges during the clustering process, thus enhancing clustering effectiveness. We conducted extensive experiments on three real-world weighted graph datasets. In particular, our model outperforms the best baseline, demonstrating its superior performance. Furthermore, experiments also show that the proposed graph contraction module can significantly reduce training time and storage space.