| A comprehensive survey on graph anomaly detection with deep learning X Ma, J Wu, S Xue, J Yang, C Zhou, QZ Sheng, H Xiong, L Akoglu IEEE Transactions on Knowledge and Data Engineering 35 (12), 12012-12038, 2021 | 447 | 2021 |
| Dagad: Data augmentation for graph anomaly detection F Liu, X Ma, J Wu, J Yang, S Xue, A Beheshti, C Zhou, H Peng, QZ Sheng, ... 2022 IEEE International Conference on Data Mining (ICDM), 259-268, 2022 | 24 | 2022 |
| Towards Graph-level Anomaly Detection via Deep Evolutionary Mapping X Ma, J Wu, J Yang, QZ Sheng SIGKDD 2023, https://openreview.net/pdf?id=sGp2yRPKoI, 2023 | 6 | 2023 |
| Deep Multi-Attributed-View Graph Representation Learning X Ma, S Xue, J Wu, J Yang, C Paris, S Nepal, QZ Sheng IEEE Transactions on Network Science and Engineering, 2022 | 4 | 2022 |
| Heterogeneous hypergraph neural network for social recommendation using attention network B Khan, J Wu, J Yang, X Ma ACM Transactions on Recommender Systems, 2023 | 3 | 2023 |
| Application of Mobile Games to Support Clinical Data Collection for Patients with Niemann-Pick Disease RO Sinnott, J Han, W Hu, X Ma, K Yu | 3 | 2015 |
| Heterogeneous graph neural network via knowledge relations for fake news detection B Xie, X Ma, J Wu, J Yang, S Xue, H Fan Proceedings of the 35th International Conference on Scientific and …, 2023 | 2 | 2023 |
| Knowledge graph enhanced heterogeneous graph neural network for fake news detection B Xie, X Ma, J Wu, J Yang, H Fan IEEE Transactions on Consumer Electronics, 2023 | 1 | 2023 |
| Heterogeneous Subgraph Transformer for Fake News Detection Y Zhang, X Ma, J Wu, J Yang, H Fan arXiv preprint arXiv:2404.13192, 2024 | | 2024 |
| New recipes for graph anomaly detection: Forward diffusion dynamics and graph generation X Ma, R Li, F Liu, K Ding, J Yang, J Wu | | 2023 |
