ExGAT: Context extended graph attention neural network.

Area of Science:

• Graph Neural Networks
• Machine Learning
• Artificial Intelligence

Background:

• Context is crucial in attention mechanisms, defining the scope of analysis.
• In attention-based GNNs, context is the set of nodes for graph embedding.
• Existing methods using immediate neighbors limit capturing long-distance dependencies.

Purpose of the Study:

• To propose a novel attention-based GNN framework with extended contexts.
• To enhance the ability of attention mechanisms to capture long-distance dependencies in graphs.
• To improve the performance of GNNs by optimizing context utilization.

Main Methods:

• Context expansion by selecting multi-hop nodes based on information transferability and hop count.
• Development of two heuristic context refinement policies to reduce computational cost and maintain local graph structure.
• Application of multi-head attention on the refined, extended context.

Main Results:

• The proposed method significantly outperforms 23 baseline approaches in numerical comparisons.
• Model analysis confirms that extending context with informative multi-hop neighbors enhances GNN performance.
• The refined context strategies effectively manage computational costs while preserving essential graph information.

Conclusions:

• The novel attention-based GNN framework with extended contexts effectively addresses limitations of traditional methods.
• The proposed context expansion and refinement strategies are crucial for improving long-distance dependency capture in GNNs.
• This approach offers a superior method for graph representation learning, particularly in complex graph structures.