HeTa: Relation-wise Heterogeneous Graph Foundation Attack Model
Yuling Wang, Zihui Chen, Pengfei Jiao, Xiao Wang

TL;DR
HeTa is a novel attack model for heterogeneous graph neural networks that leverages shared vulnerabilities across models and graphs, enabling efficient, transferable, and adaptable perturbations to evaluate and improve their robustness.
Contribution
The paper introduces HeTa, a relation-wise foundation attack model that uses a surrogate to identify shared attack units, facilitating generalizable and quick perturbations across different HGNNs and graphs.
Findings
HeTa achieves strong attack performance across various HGNNs.
The attack model demonstrates high transferability to unseen HGs.
Shared vulnerability patterns exist across different HGNNs from a relation-aware perspective.
Abstract
Heterogeneous Graph Neural Networks (HGNNs) are vulnerable, highlighting the need for tailored attacks to assess their robustness and ensure security. However, existing HGNN attacks often require complex retraining of parameters to generate specific perturbations for new scenarios. Recently, foundation models have opened new horizons for the generalization of graph neural networks by capturing shared semantics across various graph distributions. This leads us to ask:Can we design a foundation attack model for HGNNs that enables generalizable perturbations across different HGNNs, and quickly adapts to new heterogeneous graphs (HGs)? Empirical findings reveal that, despite significant differences in model design and parameter space, different HGNNs surprisingly share common vulnerability patterns from a relation-aware perspective. Therefore, we explore how to design foundation HGNN attack…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Adversarial Robustness in Machine Learning · Explainable Artificial Intelligence (XAI)
