4 papers
Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models
Bin Ma, Yuyuan Feng, Minhua Lin +1
Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial analysis, leading to a growing demand f…
Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks
Yuxiang Zhang, Bin Ma, Enyan Dai
Graph Neural Networks (GNNs) have achieved remarkable results in various tasks. Recent studies reveal that graph backdoor attacks can poison the GNN model to predict test nodes wit…
General Protein Pretraining or Domain-Specific Designs? Benchmarking Protein Modeling on Realistic Applications
Shuo Yan, Yuliang Yan, Bin Ma +6
Recently, extensive deep learning architectures and pretraining strategies have been explored to support downstream protein applications. Additionally, domain-specific models incor…
Backdoor or Manipulation? Graph Mixture of Experts Can Defend Against Various Graph Adversarial Attacks
Yuyuan Feng, Bin Ma, Enyan Dai
Extensive research has highlighted the vulnerability of graph neural networks (GNNs) to adversarial attacks, including manipulation, node injection, and the recently emerging threa…