Controlling representation evolution in deep graph neural networks

Area of Science:

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks

Background:

• Deep Graph Neural Networks (GNNs) face performance degradation with increased propagation layers.
• This degradation is often attributed to over-smoothing, but a deeper analysis reveals issues with representation trajectories.

Purpose of the Study:

• To address deep GNN degradation by formulating it as a representation trajectory control problem.
• To introduce a novel framework that integrates complementary controls for stable and traceable GNN representations.

Main Methods:

• Developed a framework integrating Stable Reaction-Diffusion (SRD) encoder for trajectory stabilization.
• Incorporated a deterministic depth-normalized traceability constraint (RDC) for feature consistency.
• Introduced a Rank-Rate Constraint (RRC) to prevent subspace degeneration.

Main Results:

• The proposed framework effectively controls representation trajectories, mitigating degradation in deep GNNs.
• Experiments demonstrate improved robustness and stability compared to standard GNN architectures.
• The integration and diagnostic approach proves effective in analyzing and enhancing GNN performance.

Conclusions:

• Deep GNN degradation is a complex issue involving representation trajectory instability.
• The developed trajectory-control framework offers a robust solution for enhancing deep GNN performance.
• This work provides a new perspective and practical tools for deep GNN research and application.