Adversarial Robustness of Link Sign Prediction in Signed Graphs

Summary

Researchers show that balance theory, which signed graph neural networks (SGNNs) use to model positive and negative relationships, creates exploitable weaknesses under closed-box attacks. They propose balance-attack, which targets graph balance degree and is solved with a heuristic for an NP-hard problem, and introduce Balance Augmented-Signed Graph Contrastive Learning (BA-SGCL) to restore robustness by maintaining high balance degree in the latent space. Experiments across multiple SGNN architectures and real-world datasets report both the attack's effectiveness and BA-SGCL's improved resilience.

Mitigation

BA-SGCL, a contrastive learning framework with balance augmentation, maintains high balance degree in the latent space to improve model resilience against balance-attack.

Topics

• Adversarial machine learning

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