7 papers
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…
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…
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…
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…
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…
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…