87 citations · 305 across the 34 of their papers we have counts for
38 papers
TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex
Yuliang Yan, Shuo Yan, Haochun Tang +2
Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target pro…
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…
SCL-GNN: Towards Generalizable Graph Neural Networks via Spurious Correlation Learning
Yuxiang Zhang, Enyan Dai
Graph Neural Networks (GNNs) have demonstrated remarkable success across diverse tasks. However, their generalization capability is often hindered by spurious correlations between…
How Far Are LLMs from Professional Poker Players? Revisiting Game-Theoretic Reasoning with Agentic Tool Use
Minhua Lin, Enyan Dai, Hui Liu +11
As Large Language Models (LLMs) are increasingly applied in high-stakes domains, their ability to reason strategically under uncertainty becomes critical. Poker provides a rigorous…
FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks
Renqiang Luo, Huafei Huang, Tao Tang +5
Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in…
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…
Let's Grow an Unbiased Community: Guiding the Fairness of Graphs via New Links
Jiahua Lu, Huaxiao Liu, Shuotong Bai +3
Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications. However, due to the biases in the graph structures, graph neural networks face significan…
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…
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…
DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection
Yuliang Yan, Haochun Tang, Shuo Yan +1
Large language models (LLMs) are considered valuable Intellectual Properties (IP) for legitimate owners due to the enormous computational cost of training. It is crucial to protect…
Fairness in Augmented Graph Learning: A Survey
Renqiang Luo, Huafei Huang, Ziqi Xu +4
Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques. Examples include federated learning, graph transformers,…
LiSA: Leveraging Link Recommender to Attack Graph Neural Networks via Subgraph Injection
Wenlun Zhang, Enyan Dai, Kentaro Yoshioka
Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in modeling data with graph structures, yet recent research reveals their susceptibility to adversarial attack…
LanP: Rethinking the Impact of Language Priors in Large Vision-Language Models
Zongyu Wu, Yuwei Niu, Hongcheng Gao +12
Large Vision-Language Models (LVLMs) have shown impressive performance in various tasks. However, LVLMs suffer from hallucination, which hinders their adoption in the real world. E…
UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme Active-Site Knowledge
Chenao Li, Shuo Yan, Enyan Dai
Enzyme-catalyzed protein cleavage is essential for many biological functions. Accurate prediction of cleavage sites can facilitate various applications such as drug development, en…
Stealing Training Graphs from Graph Neural Networks
Minhua Lin, Enyan Dai, Junjie Xu +3
Graph Neural Networks (GNNs) have shown promising results in modeling graphs in various tasks. The training of GNNs, especially on specialized tasks such as bioinformatics, demands…
Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
Minhua Lin, Zhiwei Zhang, Enyan Dai +4
Graph Prompt Learning (GPL) has been introduced as a promising approach that uses prompts to adapt pre-trained GNN models to specific downstream tasks without requiring fine-tuning…
Robustness Inspired Graph Backdoor Defense
Zhiwei Zhang, Minhua Lin, Junjie Xu +3
Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable…
Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
Zhiwei Zhang, Minhua Lin, Enyan Dai +1
Graph Neural Networks (GNNs) have shown remarkable performance in various tasks. However, recent works reveal that GNNs are vulnerable to backdoor attacks. Generally, backdoor atta…
PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection
Enyan Dai, Minhua Lin, Suhang Wang
Pretraining on Graph Neural Networks (GNNs) has shown great power in facilitating various downstream tasks. As pretraining generally requires huge amount of data and computational…
Certifiably Robust Graph Contrastive Learning
Minhua Lin, Teng Xiao, Enyan Dai +2
Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph representation learning method. However, it has been shown that GCL is vulnerable to adversarial attack…
Shape-aware Graph Spectral Learning
Junjie Xu, Enyan Dai, Dongsheng Luo +2
Spectral Graph Neural Networks (GNNs) are gaining attention for their ability to surpass the limitations of message-passing GNNs. They rely on supervision from downstream tasks to…
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Enyan Dai, Limeng Cui, Zhengyang Wang +5
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data. However, recent works show that GNNs are vulnerable to adversarial attacks which can foo…
Unnoticeable Backdoor Attacks on Graph Neural Networks
Enyan Dai, Minhua Lin, Xiang Zhang +1
Graph Neural Networks (GNNs) have achieved promising results in various tasks such as node classification and graph classification. Recent studies find that GNNs are vulnerable to…
HP-GMN: Graph Memory Networks for Heterophilous Graphs
Junjie Xu, Enyan Dai, Xiang Zhang +1
Graph neural networks (GNNs) have achieved great success in various graph problems. However, most GNNs are Message Passing Neural Networks (MPNNs) based on the homophily assumption…