Exploring the role of edge distribution in graph convolutional networks

Area of Science:

• Computer Science
• Machine Learning
• Graph Neural Networks

Background:

• Graph Convolutional Networks (GCNs) excel at node representation learning using neighborhood information.
• Existing GCN models often assume graph homophily, limiting performance on heterophilous graphs.
• Neighbor selection significantly impacts GCN performance, especially in heterophilous settings.

Purpose of the Study:

• Investigate the influence of neighbor selection on GCN performance.
• Analyze edge distribution's role in GCNs.
• Propose a novel GCN model to improve performance on heterophilous graphs.

Main Methods:

• Theoretical and empirical analysis of edge distribution in GCNs.
• Development of the Graph Convolution Network with Improved Edge Distribution (GCN-IED) model.
• Evaluation on diverse graph benchmark datasets.

Main Results:

• GCN-IED effectively incorporates both direct and hidden edges for node representation.
• The proposed model demonstrates superior performance on heterophilous datasets compared to state-of-the-art methods.
• Optimized edge distribution enhances GCN capabilities.

Conclusions:

• Neighbor selection and edge distribution are critical for GCN performance.
• GCN-IED offers an effective approach for graph representation learning on heterophilous graphs.
• The findings provide valuable insights for advancing GCN research.