Publications

Conference

• Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification [PDF] [Code]
  Kaize Ding, Xiaoxiao Ma, Yixin Liu, Shirui Pan.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024.

• GOODAT: Towards Test-time Graph Out-of-Distribution Detection [PDF] [Code]
  Luzhi Wang, Di Jin, He Zhang, Yixin Liu, Dongxiao He, Wenjie Wang, Shirui Pan, Tat-Seng Chua.
  AAAI Conference on Artificial Intelligence (AAAI), 2024.

• Towards Self-Interpretable Graph-Level Anomaly Detection [PDF] [Code]
  Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan.
  Advances in Neural Information Processing Systems (NeurIPS), 2023

• PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [PDF] [Code]
  Junjun Pan*, Yixin Liu*, Yizhen Zheng*, Shirui Pan.
  IEEE International Conference on Data Mining (ICDM), 2023.

• Learning Strong Graph Neural Networks with Weak Information [PDF] [Code]
  Yixin Liu, Kaize Ding, Jianling Wang, Vincent CS Lee, Huan Liu, Shirui Pan.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023.

• Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating [PDF] [Code]
  Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent CS Lee, Shirui Pan.
  AAAI Conference on Artificial Intelligence (AAAI), 2023.

• Federated Learning on Non-IID Graphs via Structural Knowledge Sharing [PDF] [Code]
  Yue Tan*, Yixin Liu*, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang.
  AAAI Conference on Artificial Intelligence (AAAI), 2023.

• GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection [PDF] [Code]
  Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan.
  ACM International Conference on Web Search and Data Mining (WSDM), 2023.

• Towards unsupervised deep graph structure learning [PDF] [Code]
  Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan.
  The Web Conference (WWW), 2022.

• ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning [PDF] [Code]
  Ming Jin, Yixin Liu, Yu Zheng, Lianhua Chi, Yuan-Fang Li, and Shirui Pan.
  ACM International Conference on Information and Knowledge Management (CIKM), 2021.

Journal

• Integrating Graphs with Large Language Models: Methods and Prospects [PDF]
  Shirui Pan, Yizhen Zheng, Yixin Liu.
  IEEE Intelligent Systems, 2023.

• Graph Self-Supervised Learning: A Survey [PDF]
  Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, Philip S. Yu.
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.

• Anomaly Detection in Dynamic Graphs via Transformer [PDF] [Code]
  Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Qingfeng Chen, Vincent CS Lee.
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021.

• Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection [PDF] [Code]
  Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T Phan, Yi-Ping Phoebe Chen.
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021.

• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [PDF] [Code]
  Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021.

• Cyclic Label Propagation for Graph Semi-supervised Learning [PDF]
  Zhao Li, Yixin Liu, Zhen Zhang, Shirui Pan, Jianliang Gao, Jiajun Bu.
  World Wide Web (WWWJ), 2021.

Tutorial

• Graph Self-Supervised Learning: Taxonomy, Frontiers, and Applications [Slides]
  Yixin Liu, Yizhen Zheng, Ming Jin, Feng Xia, Shirui Pan.
  IEEE International Joint Conference on Neural Networks (IJCNN), 2023.

Preprint

• Towards Data-centric Graph Machine Learning: Review and Outlook [PDF]
  Xin Zheng, Yixin Liu, Zhifeng Bao, Meng Fang, Xia Hu, Alan Wee-Chung Liew, Shirui Pan.
  2023.

• Graph Neural Networks for Graphs with Heterophily: A Survey [PDF]
  Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, Philip S. Yu.
  2022.

• From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach [PDF]
  Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T Phan, Shirui Pan, Yi-Ping Phoebe Chen.
  2022.