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This paper introduces STAG, a stealthy backdoor attack framework targeting graph foundation models on text-attributed graphs, coordinating graph and text triggers to evade detection and achieve effective attacks.
This paper proposes MSB-GFM, a multi-semantic basis graph foundation model for cross-domain multi-label node classification, addressing semantic entanglement by representing nodes as adaptive compositions of semantic bases.
This arXiv paper introduces ProGFM, a Propagation-aware Graph Foundation Model that treats propagation relationships between edges and feature dimensions as transferable knowledge units, enabling adaptive aggregation and improved cross-domain generalization.
This paper identifies and attacks the alignment layer of graph foundation models, showing it is a distinct attack surface vulnerable to perturbations at low budgets, especially for spectral tokenizers, and proposes detection-based defenses.
Proposes FedGAMMA, a federated multimodal graph foundation learning framework that aligns multimodal attributes and graph topology via two-stage pre-training and prompt-based fine-tuning, achieving significant gains on multiple datasets.