| 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, 2022 | 712 | 2022 |
| 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 35 (6), 5879-5900, 2022 | 645 | 2022 |
| A comprehensive survey on community detection with deep learning X Su, S Xue, F Liu, J Wu, J Yang, C Zhou, W Hu, C Paris, S Nepal, D Jin, ... IEEE Transactions on Neural Networks and Learning Systems, 2022 | 436 | 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, 2022 | 357 | 2022 |
| Deep Learning for Community Detection: Progress, Challenges and Opportunities F Liu, S Xue, J Wu, C Zhou, W Hu, C Paris, S Nepal, J Yang, PS Yu The 29th International Joint Conference on Artificial Intelligence (IJCAI-20 …, 2020 | 303 | 2020 |
| Graph Neural Architecture Search Y Gao, H Yang, Z Peng, C Zhou, Y Hu The 29th International Joint Conference on Artificial Intelligence (IJCAI-20 …, 2020 | 204 | 2020 |
| Unsupervised Domain Adaptive Graph Convolutional Networks M Wu, S Pan, C Zhou, X Chang, X Zhu The 29th International Conference on World Wide Web (WWW-20), 1457-1467, 2020 | 199 | 2020 |
| Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning M Jin, Y Zheng, YF Li, C Gong, C Zhou, S Pan arXiv preprint arXiv:2105.05682, 2021 | 172 | 2021 |
| eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks G Zhang, Z Li, J Huang, J Wu, C Zhou, J Yang ACM Transactions on Information Systems, 2022 | 146 | 2022 |
| Relation Structure-Aware Heterogeneous Graph Neural Network S Zhu, C Zhou, S Pan, X Zhu, B Wang The 19th IEEE International Conference on Data Mining (ICDM-19), 1534-1549, 2019 | 138 | 2019 |
| On the upper bounds of spread for greedy algorithms in social network influence maximization C Zhou, P Zhang, W Zang, L Guo IEEE Transactions on Knowledge and Data Engineering 27 (10), 2770-2783, 2015 | 134 | 2015 |
| Personalized influence maximization on social networks J Guo, P Zhang, C Zhou, Y Cao, L Guo Proceedings of the 22nd ACM international conference on Information …, 2013 | 133 | 2013 |
| FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance G Zhang, J Wu, J Yang, A Beheshti, S Xue, C Zhou, QZ Sheng The 21th IEEE International Conference on Data Mining (ICDM-21), 873-882, 2021 | 123 | 2021 |
| GraphNAS: Graph Neural Architecture Search with Reinforcement Learning Y Gao, H Yang, P Zhang, C Zhou, Y Hu arXiv preprint arXiv:1904.09981, 2019 | 119 | 2019 |
| Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning G Wan, S Pan, C Gong, C Zhou, G Haffari The 29th International Joint Conference on Artificial Intelligence (IJCAI-20 …, 2020 | 111 | 2020 |
| H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections F Shi, Y Cao, Y Shang, Y Zhou, C Zhou, J Wu Proceedings of the ACM Web Conference 2022, 1486-1494, 2022 | 109 | 2022 |
| Graph geometry interaction learning S Zhu, S Pan, C Zhou, J Wu, Y Cao, B Wang Advances in Neural Information Processing Systems 33, 7548-7558, 2020 | 104 | 2020 |
| Ublf: An upper bound based approach to discover influential nodes in social networks C Zhou, P Zhang, J Guo, X Zhu, L Guo 2013 IEEE 13th International Conference on Data Mining, 907-916, 2013 | 103 | 2013 |
| Active discriminative network representation learning L Gao, H Yang, C Zhou, J Wu, S Pan, Y Hu Proceedings of the 27th International Joint Conference on Artificial …, 2018 | 101 | 2018 |
| Comparative study between incremental and ensemble learning on data streams: Case study W Zang, P Zhang, C Zhou, L Guo Journal of Big Data 1 (1), 5, 2014 | 101 | 2014 |