Graph self-supervised learning: A survey Y Liu, M Jin, S Pan, C Zhou, Y Zheng, F Xia, SY Philip IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022 | 524 | 2022 |
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 | 274 | 2021 |
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 | 200 | 2022 |
Towards unsupervised deep graph structure learning Y Liu, Y Zheng, D Zhang, H Chen, H Peng, S Pan ACM Web Conference (WWW), 2022 | 148 | 2022 |
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 | 104 | 2021 |
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 | 92 | 2023 |
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 | 87 | 2021 |
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 | 84 | 2021 |
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 | 53 | 2023 |
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 | 48 | 2024 |
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 | 48 | 2023 |
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 | 28 | 2023 |
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 | 26 | 2023 |
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 | 19 | 2023 |
Integrating graphs with large language models: Methods and prospects S Pan, Y Zheng, Y Liu IEEE Intelligent Systems, 2024 | 18 | 2024 |
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 | 17 | 2022 |
Cyclic label propagation for graph semi-supervised learning Z Li, Y Liu, Z Zhang, S Pan, J Gao, J Bu World Wide Web, 2022 | 8 | 2022 |
ARC: A generalist graph anomaly detector with in-context learning Y Liu, S Li, Y Zheng, Q Chen, C Zhang, S Pan arXiv preprint arXiv:2405.16771, 2024 | 4 | 2024 |
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 | 4 | 2023 |
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 arXiv preprint arXiv:2406.15523, 2024 | 3 | 2024 |