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Yixin Liu
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Cited by
Year
Graph self-supervised learning: A survey
Y Liu, S Pan, M Jin, C Zhou, F Xia, PS Yu
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022
4022022
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), 2021
2132021
Graph neural networks for graphs with heterophily: A survey
X Zheng, Y Liu, S Pan, M Zhang, D Jin, PS Yu
arXiv preprint arXiv:2202.07082, 2022
1392022
Towards unsupervised deep graph structure learning
Y Liu, Y Zheng, D Zhang, H Chen, H Peng, S Pan
ACM Web Conference (WWW), 2022
1132022
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
702021
Anomaly detection in dynamic graphs via transformer
Y Liu, S Pan, YG Wang, F Xiong, L Wang, Q Chen, V Lee
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
632021
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
612021
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
542023
Beyond smoothing: Unsupervised graph representation learning with edge heterophily discriminating
Y Liu, Y Zheng, D Zhang, V Lee, S Pan
AAAI Conference on Artificial Intelligence (AAAI), 2023
342023
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
332024
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
312023
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
182023
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
102023
From unsupervised to few-shot graph anomaly detection: A multi-scale contrastive learning approach
Y Zheng, M Jin, Y Liu, L Chi, KT Phan, S Pan, YPP Chen
arXiv preprint arXiv:2202.05525, 2022
102022
A novel method of hyperspectral data classification based on transfer learning and deep belief network
K Li, M Wang, Y Liu, N Yu, W Lan
Applied Sciences, 2019
102019
MRD-NETs: Multi-scale residual networks with dilated convolutions for classification and clustering analysis of spacecraft electrical signal
Y Liu, K Li, Y Zhang, S Song
IEEE Access, 2019
72019
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
62023
Integrating graphs with large language models: Methods and prospects
S Pan, Y Zheng, Y Liu
IEEE Information Systems, 2023
62023
Cyclic label propagation for graph semi-supervised learning
Z Li, Y Liu, Z Zhang, S Pan, J Gao, J Bu
World Wide Web, 2022
62022
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
2024
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