4 citations · 10 across the 5 of their papers we have counts for
5 papers
Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning
Jinyin Chen, Wenbo Mu, Luxin Zhang +3
Graph neural network (GNN) has captured wide attention due to its capability of graph representation learning for graph-structured data. However, the distributed data silos limit t…
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs
Haibin Zheng, Haiyang Xiong, Jinyin Chen +2
Graph neural network (GNN) with a powerful representation capability has been widely applied to various areas, such as biological gene prediction, social recommendation, etc. Recen…
Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection
Haibin Zheng, Haiyang Xiong, Haonan Ma +2
Link prediction, inferring the undiscovered or potential links of the graph, is widely applied in the real-world. By facilitating labeled links of the graph as the training data, n…
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning
Jinyin Chen, Guohan Huang, Haibin Zheng +3
Graph neural network (GNN) has achieved great success on graph representation learning. Challenged by large scale private data collected from user-side, GNN may not be able to refl…
Graphfool: Targeted Label Adversarial Attack on Graph Embedding
Jinyin Chen, Xiang Lin, Dunjie Zhang +4
Deep learning is effective in graph analysis. It is widely applied in many related areas, such as link prediction, node classification, community detection, and graph classificatio…