RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation

TL;DR

This paper introduces RSEA-MVGNN, a multi-view graph neural network that enhances structural diversity and aggregates views based on quality, leading to improved performance on real-world datasets.

Contribution

The paper proposes a novel GNN framework that estimates view uncertainty and quality, enabling reliable structural enhancement and quality-based view aggregation.

Findings

Outperforms state-of-the-art GNN methods on five datasets.

Achieves diverse feature representation through view-specific structural enhancement.

Effectively evaluates view quality to improve aggregation and learning.

Abstract

Graph Neural Networks (GNNs) have exhibited remarkable efficacy in learning from multi-view graph data. In the framework of multi-view graph neural networks, a critical challenge lies in effectively combining diverse views, where each view has distinct graph structure features (GSFs). Existing approaches to this challenge primarily focus on two aspects: 1) prioritizing the most important GSFs, 2) utilizing GNNs for feature aggregation. However, prioritizing the most important GSFs can lead to limited feature diversity, and existing GNN-based aggregation strategies equally treat each view without considering view quality. To address these issues, we propose a novel Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation (RSEA-MVGNN). Firstly, we estimate view-specific uncertainty employing subjective logic. Based on this uncertainty, we design reliable structural enhancement by feature de-correlation algorithm. This approach enables each enhancement to focus on different GSFs, thereby achieving diverse feature representation in the enhanced structure. Secondly, the model learns view-specific beliefs and uncertainty as opinions, which are utilized to evaluate view quality. Based on these opinions, the model enables high-quality views to dominate GNN aggregation, thereby facilitating representation learning. Experimental results conducted on five real-world datasets demonstrate that RSEA-MVGNN outperforms several state-of-the-art GNN-based methods.