Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering

TL;DR

This paper introduces DOAGC, a novel multi-view graph clustering method that reconstructs graph structures to effectively handle heterophilous graphs while preserving the benefits of traditional GNNs.

Contribution

The paper proposes a dual-optimized adaptive graph reconstruction approach that enhances traditional GNNs for heterophilous multi-view graph clustering.

Findings

Effectively mitigates heterophilous graph issues

Outperforms existing methods in clustering accuracy

Validates the approach through extensive experiments

Abstract

Multi-view clustering is an important machine learning task for multi-media data, encompassing various domains such as images, videos, and texts. Moreover, with the growing abundance of graph data, the significance of multi-view graph clustering (MVGC) has become evident. Most existing methods focus on graph neural networks (GNNs) to extract information from both graph structure and feature data to learn distinguishable node representations. However, traditional GNNs are designed with the assumption of homophilous graphs, making them unsuitable for widely prevalent heterophilous graphs. Several techniques have been introduced to enhance GNNs for heterophilous graphs. While these methods partially mitigate the heterophilous graph issue, they often neglect the advantages of traditional GNNs, such as their simplicity, interpretability, and efficiency. In this paper, we propose a novel multi-view graph clustering method based on dual-optimized adaptive graph reconstruction, named DOAGC. It mainly aims to reconstruct the graph structure adapted to traditional GNNs to deal with heterophilous graph issues while maintaining the advantages of traditional GNNs. Specifically, we first develop an adaptive graph reconstruction mechanism that accounts for node correlation and original structural information. To further optimize the reconstruction graph, we design a dual optimization strategy and demonstrate the feasibility of our optimization strategy through mutual information theory. Numerous experiments demonstrate that DOAGC effectively mitigates the heterophilous graph problem.