About Me
Hi there! I am Yixin Liu, a research fellow at the Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Australia, advised by Prof. Shirui Pan. Previous to that, I obtained my Ph.D. degree from the Faculty of Information Technology, Monash University, Australia in 2024, and obtained my B.S.&M.E. degree from Beihang University, China in 2017 and 2020, respectively. My research interests are in graph representation learning and graph neural networks, with a special focus on unsupervised and weak-supervised scenarios. I am a recipient of the Google Ph.D. Fellowship in 2022.
News
- 2024/09: Our paper on generalist graph anomaly detection with in-context learning has been accepted by NeurIPS 2024.
- 2024/08: Our paper on data-efficient graph learning has been accepted by AI Magazine.
- 2024/07: Our paper on graph representation learning has been accepted by CIKM 2024.
- 2024/07: Our paper on data imputation has been accepted by CIKM 2024.
- 2024/05: Our paper on GNN against label noise has been accepted by KDD 2024.
- 2024/03: Our paper on diffusion model for data imputation has been accepted by ICLR 2024 GenAI4DM Workshop.
- 2024/02: Our survey on federated learning has been accepted by IJMLC.
- 2023/12: Our paper on graph OOD detection has been accepted by AAAI 2024.
- 2023/11: I give an invited talk at LoG Conference 2023 Shanghai meetup.
- 2023/11: Our paper on graph+LLM has been accepted by IEEE Intelligent Systems.
- 2023/09: Our paper on explainable graph anomaly detection has been accepted by NeurIPS 2023.
- 2023/09: Our survey on data-centric graph machine learning is now on arXiv.
- 2023/09: Our paper on graph anomaly detection has been accepted by ICDM 2023.
- 2023/06: We present a tutorial on graph self-supervised learning at IJCNN 2023.
- 2023/05: Our paper on weak information graph learning has been accepted by KDD 2023.
- 2022/11: Our paper on graph representation learning has been accepted by AAAI 2023.
- 2022/11: Our paper on federated graph learning has been accepted by AAAI 2023.
- 2022/10: Our paper on graph OOD detection has been accepted by WSDM 2023.
- 2022/08: I am honored to receive the Google Ph.D. Fellowship in 2022.
- 2022/05: Our survey on graph self-supervised learning has been accepted by IEEE TKDE.
- 2022/01: Our paper on graph structure learning has been accepted by WWW 2022.
- 2021/11: Our paper on dynamic graph has been accepted by IEEE TKDE.
- 2021/10: Our paper on graph anomaly detection has been accepted by IEEE TKDE.
- 2021/08: Our paper on graph anomaly detection has been accepted by CIKM 2021.
- 2021/06: Our paper on label propagation has been accepted by World Wide Web.
- 2021/03: Our paper on graph anomaly detection has been accepted by IEEE TNNLS.
Selected Papers (first-author/co-first-author)
- ARC: A Generalist Graph Anomaly Detector with in-Context Learning
- Yixin Liu, Shiyuan Li, Yu Zheng, Qingfeng Chen, Chengqi Zhang, Shirui Pan
- Advances in Neural Information Processing Systems (NeurIPS), 2024
- [Paper] [Code]
- Self-supervision Improves Diffusion Models for Tabular Data Imputation
- Yixin Liu, Thalaiyasingam Ajanthan, Hisham Husain, Vu Nguyen
- ACM International Conference on Information & Knowledge Management (CIKM), 2024
- [Paper] [Code]
- Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation
- Shiyuan Li*, Yixin Liu*, Qingfeng Chen, Geoffrey I Webb, Shirui Pan
- ACM International Conference on Information & Knowledge Management (CIKM), 2024
- [Paper] [Code]
- Towards Self-Interpretable Graph-Level Anomaly Detection
- Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan
- Advances in Neural Information Processing Systems (NeurIPS), 2023
- [Paper] [Code]
- Learning Strong Graph Neural Networks with Weak Information
- Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, Shirui Pan
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023
- [Paper] [Code]
- Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
- Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent Lee, Shirui Pan
- AAAI Conference on Artificial Intelligence (AAAI), 2023 (Oral)
- [Paper] [Code]
- Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
- Yue Tan*, Yixin Liu*, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang
- AAAI Conference on Artificial Intelligence (AAAI), 2023 (Oral)
- [Paper] [Code]
- GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
- Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan
- ACM International Conference on Web Search and Data Mining (WSDM), 2023 (Oral)
- [Paper] [Code]
- Graph Self-Supervised Learning: A Survey
- Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, Philip S. Yu
- IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022
- [Paper]
- Towards Unsupervised Deep Graph Structure Learning
- Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan
- The Web Conference (WWW), 2022 (Best Paper Award candidate)
- [Paper] [Code]
- Anomaly Detection in Dynamic Graphs via Transformer
- Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Qingfeng Chen, Vincent Lee
- IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
- [Paper] [Code]
- Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
- Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
- [Paper] [Code]
Education
Ph.D. (2021-2024) in Monash University
M.S. (2017-2020) in Beihang University
B.S. (2013-2017) in Beihang University
Experience
Griffith University, ARC Research Fellow, 2024-present.
Amazon, Applied Scientist Intern, 2023.
Monash University, Research Assistant, 2021.
Alibaba, Research Intern, 2020.
Contact
Email: yixin.liu[at]griffith[dot]edu[dot]au
Office: G23 2.38, 1 Parkland Dr, Southport, QLD 4215