422 citations · 815 across the 32 of their papers we have counts for
7 papers · 1 filter
Fast Graph Condensation with Structure-based Neural Tangent Kernel
Lin Wang, Wenqi Fan, Jiatong Li +2
The rapid development of Internet technology has given rise to a vast amount of graph-structured data. Graph Neural Networks (GNNs), as an effective method for various graph mining…
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
Zhikai Chen, Haitao Mao, Hang Li +8
Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily…
Generative Diffusion Models on Graphs: Methods and Applications
Chengyi Liu, Wenqi Fan, Yunqing Liu +5
Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video…
Fairness Reprogramming
Guanhua Zhang, Yihua Zhang, Yang Zhang +4
Despite a surge of recent advances in promoting machine Learning (ML) fairness, the existing mainstream approaches mostly require retraining or finetuning the entire weights of the…
Knowledge-enhanced Black-box Attacks for Recommendations
Jingfan Chen, Wenqi Fan, Guanghui Zhu +4
Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles…
Defense Against Gradient Leakage Attacks via Learning to Obscure Data
Yuxuan Wan, Han Xu, Xiaorui Liu +3
Federated learning is considered as an effective privacy-preserving learning mechanism that separates the client's data and model training process. However, federated learning is s…
Jointly Attacking Graph Neural Network and its Explanations
Wenqi Fan, Wei Jin, Xiaorui Liu +7
Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adve…