BAB-GSL: Using Bayesian influence with attention mechanism to optimize graph structure in basic views.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Network Science

Background:

• Graph Neural Networks (GNNs) are increasingly important for analyzing complex data.
• Graph Structure Learning (GSL) aims to optimize graph representations for better GNN performance.
• Existing GSL methods face challenges in creating accurate and robust node relationship graphs.

Purpose of the Study:

• To introduce BAB-GSL, a novel approach for approximating ideal graph structures.
• To enhance the accuracy and robustness of graph representations for GNNs.
• To improve network training performance through optimized graph structures.

Main Methods:

• The BAB-GSL method extracts two basic views from the original graph.
• A view fusion module generates a preliminary optimized view.
• Attention mechanisms enhance node connectivity, and a Bayesian optimizer refines the final graph structure.

Main Results:

• Experiments on multiple datasets demonstrated the effectiveness of BAB-GSL.
• The method showed robustness in both undisturbed and attacked scenarios.
• BAB-GSL successfully approximated ideal graph structures, improving performance.

Conclusions:

• BAB-GSL offers a systematic and effective approach to Graph Structure Learning.
• The proposed method enhances node relationship representation and network training.
• BAB-GSL demonstrates significant potential for GNN applications requiring high-quality graph structures.