1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 1 cited
Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
Zihan Luo, Hong Huang, Yongkang Zhou +3
Despite the remarkable capabilities demonstrated by Graph Neural Networks (GNNs) in graph-related tasks, recent research has revealed the fairness vulnerabilities in GNNs when faci…
cs.AI2024★ 1 cited
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
Zihan Luo, Xiran Song, Hong Huang +5
Improving the general capabilities of large language models (LLMs) is an active research topic. As a common data structure in many real-world domains, understanding graph data is a…