Publications

Preprint

• Blindguard: Safeguarding llm-based multi-agent systems under unknown attacks
  Rui Miao, Yixin Liu, Yili Wang, Xu Shen, Yue Tan, Yiwei Dai, Shirui Pan, Xin Wang.
  Arxiv, 2025.
  
• Raising the bar in graph ood generalization: Invariant learning beyond explicit environment modeling
  Xu Shen, Yixin Liu, Yili Wang, Rui Miao, Yiwei Dai, Shirui Pan, Yi Chang, Xin Wang.
  Arxiv, 2025.
  
• Graph Defense Diffusion Model
  Xin He, Wenqi Fan, Yili Wang, Chengyi Liu, Rui Miao, Xin Juan, Xin Wang.
  Arxiv, 2025.
  

Conference/Journal Papers (* indicates equal contributions)

Before 2025

• Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems
  Xu Shen, Yixin Liu, Yiwei Dai, Yili Wang, Rui Miao, Yue Tan, Shirui Pan, Xin Wang.
  EMNLP, 2025.
  
• AdaGCL+: An Adaptive Subgraph Contrastive Learning Towards Tackling Topological Bias
  Yili Wang, Yaohua Liu, Ninghao Liu, Rui Miao, Ying Wang, Xin Wang.
  TPAMI, 2025.
  
• Mamba-based graph convolutional networks: Tackling over-smoothing with selective state space
  Xin He, Yili Wang, Wenqi Fan, Xu Shen, Xin Juan, Rui Miao, Xin Wang.
  IJCAI, 2025.
  
• Unifying unsupervised graph-level anomaly detection and out-of-distribution detection: A benchmark
  Yili Wang, Yixin Liu, Xu Shen, Chenyu Li, Kaize Ding, Rui Miao, Ying Wang, Shirui Pan, Xin Wang.
  ICLR, 2025.
  
• Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
  Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu, Ying Wang, Xin Wang.
  ICML, 2024.
  
• Graph Contrastive Learning with Group Whitening
  Chunhui Zhang, Rui Miao.
  ACML, 2023.
  
• Adagcl: Adaptive subgraph contrastive learning to generalize large-scale graph training
  Yili Wang, Kaixiong Zhou, Rui Miao, Ninghao Liu, Xin Wang.
  CIKM, 2022.
  
• Negative samples selecting strategy for graph contrastive learning
  Rui Miao, Yintao Yang, Yao Ma, Xin Juan, Haotian Xue, Jiliang Tang, Ying Wang, Xin Wang.
  Information Sciences, 2022.
  
• Contrastive graph convolutional networks with adaptive augmentation for text classification
  Yintao Yang, Rui Miao, Yili Wang, Xin Wang.
  Information Processing & Management, 2022.
  
• Feature selection may improve deep neural networks for the bioinformatics problems
  Zheng Chen, Meng Pang, Zixin Zhao, Shuainan Li, Rui Miao, Yifan Zhang, Xiaoyue Feng, Xin Feng, Yexian Zhang, Meiyu Duan, Lan Huang, Fengfeng Zhou.
  Bioinformatics, 2020.