Proactive structural stabilization for robust learning on heterogeneous graphs

Area of Science:

• Graph Neural Networks
• Machine Learning
• Network Science

Background:

• Heterogeneous graph neural networks (HGNNs) leverage meta-path modeling for rich semantic understanding.
• However, HGNNs exhibit prediction instability under structural perturbations, limiting their real-world applicability.
• Existing robustness methods often rely on restrictive assumptions about perturbation types, hindering generalization.

Purpose of the Study:

• To propose a novel proactive framework, PSTABLE, for enhancing the robustness of HGNNs against structural perturbations.
• To address the limitations of existing methods by offering a more generalized approach to structural stabilization.
• To improve the reliability and stability of HGNN predictions in the presence of adversarial attacks.

Main Methods:

• PSTABLE employs a vulnerability assessment to identify nodes susceptible to structural changes.
• It introduces learnable auxiliary nodes along critical meta-paths to reinforce vulnerable graph regions.
• Auxiliary node representations are optimized using prediction confidence feedback, preserving semantic integrity.

Main Results:

• PSTABLE significantly enhances robustness against diverse structural perturbations, including poisoning and evasion attacks.
• The framework demonstrates consistent performance improvements across various attack scenarios.
• PSTABLE maintains competitive predictive performance on unperturbed, clean graphs.

Conclusions:

• PSTABLE offers an effective proactive strategy for improving the robustness of HGNNs.
• The method provides a generalized solution for structural stabilization in heterogeneous graphs.
• PSTABLE enhances model resilience without compromising performance on clean data.