Weighted Graph Clustering via Scale Contraction and Graph Structure Learning

2026-01-24

AI Summary

This work proposes a contraction-based, edge-weight-aware graph clustering network to address the limitations of existing methods, which often fail to effectively leverage edge weight information and suffer from high computational and memory costs as well as sensitivity to noisy edges in large-scale graphs. The proposed approach jointly optimizes the clustering objective and edge weight learning for the first time. It employs a clustering-oriented graph contraction module to substantially reduce graph scale while preserving essential structural information, and introduces an edge-weight-aware attention mechanism to identify and suppress spurious connections. Extensive experiments on three real-world weighted graph datasets demonstrate that the method significantly outperforms state-of-the-art baselines, achieving superior clustering performance while markedly reducing both training time and memory consumption.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• weighted graph clustering
• graph scale reduction
• noisy edge weights
• edge weight utilization
• graph clustering

Innovation

• graph clustering
• graph contraction
• edge-weight-aware attention
• noise mitigation
• scale reduction