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Yixin Liu
Yixin Liu
Research Fellow, School of ICT, Griffith University
Verified email at griffith.edu.au - Homepage
Title
Cited by
Cited by
Year
Graph self-supervised learning: A survey
Y Liu, M Jin, S Pan, C Zhou, Y Zheng, F Xia, PS Yu
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022
6642022
Anomaly detection on attributed networks via contrastive self-supervised learning
Y Liu, Z Li, S Pan, C Gong, C Zhou, G Karypis
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
3712022
Graph neural networks for graphs with heterophily: A survey
X Zheng, Y Wang, Y Liu, M Li, M Zhang, D Jin, PS Yu, S Pan
arXiv preprint arXiv:2202.07082, 2022
2902022
Towards unsupervised deep graph structure learning
Y Liu, Y Zheng, D Zhang, H Chen, H Peng, S Pan
ACM Web Conference (WWW), 2022
2002022
Generative and contrastive self-supervised learning for graph anomaly detection
Y Zheng, M Jin, Y Liu, L Chi, KT Phan, YPP Chen
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
1532021
Federated learning on non-iid graphs via structural knowledge sharing
Y Tan, Y Liu, G Long, J Jiang, Q Lu, C Zhang
AAAI Conference on Artificial Intelligence (AAAI), 2023
1492023
Anemone: Graph anomaly detection with multi-scale contrastive learning
M Jin, Y Liu, Y Zheng, L Chi, YF Li, S Pan
ACM International Conference on Information & Knowledge Management (CIKM), 2021
1282021
Anomaly detection in dynamic graphs via transformer
Y Liu, S Pan, YG Wang, F Xiong, L Wang, Q Chen, VCS Lee
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
1212021
Emerging trends in federated learning: From model fusion to federated X learning
S Ji, Y Tan, T Saravirta, Z Yang, Y Liu, L Vasankari, S Pan, G Long, ...
International Journal of Machine Learning and Cybernetics, 2024
882024
Beyond smoothing: Unsupervised graph representation learning with edge heterophily discriminating
Y Liu, Y Zheng, D Zhang, VCS Lee, S Pan
AAAI Conference on Artificial Intelligence (AAAI), 2023
852023
GOOD-D: On unsupervised graph out-of-distribution detection
Y Liu, K Ding, H Liu, S Pan
ACM International Conference on Web Search and Data Mining (WSDM), 2023
722023
Towards self-interpretable graph-level anomaly detection
Y Liu, K Ding, Q Lu, F Li, LY Zhang, S Pan
Advances in Neural Information Processing Systems (NeurIPS), 2023
502023
Learning strong graph neural networks with weak information
Y Liu, K Ding, J Wang, V Lee, H Liu, S Pan
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023
462023
Towards data-centric graph machine learning: Review and outlook
X Zheng, Y Liu, Z Bao, M Fang, X Hu, AWC Liew, S Pan
arXiv preprint arXiv:2309.10979, 2023
302023
Integrating graphs with large language models: Methods and prospects
S Pan, Y Zheng, Y Liu
IEEE Intelligent Systems, 2024
282024
From unsupervised to few-shot graph anomaly detection: A multi-scale contrastive learning approach
Y Zheng, M Jin, Y Liu, L Chi, KT Phan, YPP Chen
arXiv preprint arXiv:2202.05525, 2022
202022
PREM: A simple yet effective approach for node-level graph anomaly detection
J Pan, Y Liu, Y Zheng, S Pan
IEEE International Conference on Data Mining (ICDM), 2023
192023
Unifying unsupervised graph-level anomaly detection and out-of-distribution detection: A benchmark
Y Wang, Y Liu, X Shen, C Li, K Ding, R Miao, Y Wang, S Pan, X Wang
International Conference on Learning Representations (ICLR), 2025
122025
ARC: A generalist graph anomaly detector with in-context learning
Y Liu, S Li, Y Zheng, Q Chen, C Zhang, S Pan
Advances in Neural Information Processing Systems (NeurIPS), 2024
112024
GOODAT: Towards test-time graph out-of-distribution detection
L Wang, D He, H Zhang, Y Liu, W Wang, S Pan, D Jin, TS Chua
AAAI Conference on Artificial Intelligence (AAAI), 2024
112024
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Articles 1–20