Tri-Learn Graph Fusion Network for Attributed Graph Clustering

TL;DR

This paper introduces Tri-GFN, a novel deep clustering framework combining GCN, Autoencoder, and Graph Transformer with a tri-learning mechanism and feature fusion, significantly improving attributed graph clustering performance.

Contribution

The paper proposes a new tri-learning graph fusion network that effectively integrates multiple modules for enhanced attributed graph clustering, addressing over-smoothing and heterogeneity issues.

Findings

Achieves higher clustering accuracy on multiple datasets.

Surpasses state-of-the-art methods in attributed graph clustering.

Demonstrates applicability to news classification and topic retrieval.

Abstract

In recent years, models based on Graph Convolutional Networks (GCN) have made significant strides in the field of graph data analysis. However, challenges such as over-smoothing and over-compression remain when handling large-scale and complex graph datasets, leading to a decline in clustering quality. Although the Graph Transformer architecture has mitigated some of these issues, its performance is still limited when processing heterogeneous graph data. To address these challenges, this study proposes a novel deep clustering framework that comprising GCN, Autoencoder (AE), and Graph Transformer, termed the Tri-Learn Graph Fusion Network (Tri-GFN). This framework enhances the differentiation and consistency of global and local information through a unique tri-learning mechanism and feature fusion enhancement strategy. The framework integrates GCN, AE, and Graph Transformer modules. These components are meticulously fused by a triple-channel enhancement module, which maximizes the use of both node attributes and topological structures, ensuring robust clustering representation. The tri-learning mechanism allows mutual learning among these modules, while the feature fusion strategy enables the model to capture complex relationships, yielding highly discriminative representations for graph clustering. It surpasses many state-of-the-art methods, achieving an accuracy improvement of approximately 0.87% on the ACM dataset, 14.14 % on the Reuters dataset, and 7.58 % on the USPS dataset. Due to its outstanding performance on the Reuters dataset, Tri-GFN can be applied to automatic news classification, topic retrieval, and related fields.