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cs.AI2026
Can LLMs Fool Graph Learning? Exploring Universal Adversarial Attacks on Text-Attributed Graphs
Zihui Chen, Yuling Wang, Pengfei Jiao +4
Text-attributed graphs (TAGs) enhance graph learning by integrating rich textual semantics and topological context for each node. While boosting expressiveness, they also expose ne…
cs.AI2025
HeTa: Relation-wise Heterogeneous Graph Foundation Attack Model
Yuling Wang, Zihui Chen, Pengfei Jiao +1
Heterogeneous Graph Neural Networks (HGNNs) are vulnerable, highlighting the need for tailored attacks to assess their robustness and ensure security. However, existing HGNN attack…
cs.AI2025
LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph
Tu Ao, Yanhua Yu, Yuling Wang +7
Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning. However, delays in knowledge updates may cause them to reason incorrectly o…