5 papers
Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
Jiayi Luo, Qingyun Sun, Yuecen Wei +3
Multi-domain graph pre-training has emerged as a pivotal technique in developing graph foundation models. While it greatly improves the generalization of graph neural networks, its…
Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning
Yudan Song, Yuecen Wei, Yuhang Lu +6
Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improve…
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
Jinyan Wang, Liu Yang, Yuecen Wei +5
Graph Neural Network-based methods face privacy leakage risks due to the introduction of topological structures about the targets, which allows attackers to bypass the target's pri…
Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding
Tianyu Chen, Xingcheng Fu, Yisen Gao +5
Modern vision-language models (VLMs) develop patch embedding and convolution backbone within vector space, especially Euclidean ones, at the very founding. When expanding VLMs to a…
Prompt-based Unifying Inference Attack on Graph Neural Networks
Yuecen Wei, Xingcheng Fu, Lingyun Liu +3
Graph neural networks (GNNs) provide important prospective insights in applications such as social behavior analysis and financial risk analysis based on their powerful learning ca…