3 papers
cs.LG2025
Unveiling the Vulnerability of Graph-LLMs: An Interpretable Multi-Dimensional Adversarial Attack on TAGs
Bowen Fan, Zhilin Guo, Xunkai Li +5
Graph Neural Networks (GNNs) have become a pivotal framework for modeling graph-structured data, enabling a wide range of applications from social network analysis to molecular che…
cs.LG2025
Toward Scalable Graph Unlearning: A Node Influence Maximization based Approach
Xunkai Li, Bowen Fan, Zhengyu Wu +3
Machine unlearning, as a pivotal technology for enhancing model robustness and data privacy, has garnered significant attention in prevalent web mining applications, especially in…
cs.LG2025
OpenGU: A Comprehensive Benchmark for Graph Unlearning
Bowen Fan, Yuming Ai, Xunkai Li +3
Graph Machine Learning is essential for understanding and analyzing relational data. However, privacy-sensitive applications demand the ability to efficiently remove sensitive info…