About Me

I am a final-year Ph.D. student at Monash University, supervised by Prof. Shirui Pan, Dr. Daokun Zhang, and A/Prof. Vincent Lee. I obtained my B.S. degree and M.S. degree from Beihang University 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

• 2024/03: 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 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)

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-present) in Monash University

• M.S. (2017-2020) in Beihang University

• B.S. (2013-2017) in Beihang University

Experience

• Amazon, Applied Scientist Intern, 2023.

• Monash University, Research Assistant, 2021.

• Alibaba, Research Intern, 2020.

Contact

• Email: yixin.liu[at]monash[dot]edu

• Office: Level 2, 25 Exhibition Walk, Clayton, VIC 3800