72 citations · 202 across the 15 of their papers we have counts for
8 papers · 1 filter
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees
Binghui Wang, Youqi Li, Pan Zhou
Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works h…
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks
Jiaming Mu, Binghui Wang, Qi Li +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph structure related tasks such as node classification and graph classification. However, GNNs…
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
Binghui Wang, Jiayi Guo, Ang Li +2
Learning with graphs has attracted significant attention recently. Existing representation learning methods on graphs have achieved state-of-the-art performance on various graph-re…
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks
Binghui Wang, Jinyuan Jia, Neil Zhenqiang Gong
Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user's private attribute inference in…
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Jingwei Sun, Ang Li, Binghui Wang +3
Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. However, recent works demonstrated that sh…
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
Binghui Wang, Ang Li, Hai Li +1
Graph-based semi-supervised node classification (GraphSSC) has wide applications, ranging from networking and security to data mining and machine learning, etc. However, existing c…