2 citations · 2 across the 2 of their papers we have counts for
2 papers
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.LG2024★ 2 cited
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
Fan Xu, Nan Wang, Hao Wu +7
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to…