| Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis X Guo, L Chen, C Shen Measurement 93, 490-502, 2016 | 840 | 2016 |
| Graph neural networks: foundation, frontiers and applications L Wu, P Cui, J Pei, L Zhao, X Guo Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 474 | 2022 |
| Graph neural networks for natural language processing: A survey L Wu, Y Chen, K Shen, X Guo, H Gao, S Li, J Pei, B Long Foundations and Trends® in Machine Learning 16 (2), 119-328, 2023 | 361 | 2023 |
| A systematic survey on deep generative models for graph generation X Guo, L Zhao IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (5), 5370-5390, 2022 | 189 | 2022 |
| Deep fault recognizer: An integrated model to denoise and extract features for fault diagnosis in rotating machinery X Guo, C Shen, L Chen Applied Sciences 7 (1), 41, 2016 | 91 | 2016 |
| A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery W You, C Shen, X Guo, X Jiang, J Shi, Z Zhu Advances in Mechanical Engineering 9 (6), 1687814017704146, 2017 | 63 | 2017 |
| Interpretable deep graph generation with node-edge co-disentanglement X Guo, L Zhao, Z Qin, L Wu, A Shehu, Y Ye Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 55 | 2020 |
| Graphgt: Machine learning datasets for graph generation and transformation Y Du, S Wang, X Guo, H Cao, S Hu, J Jiang, A Varala, A Angirekula, ... Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 52 | 2021 |
| Generating tertiary protein structures via interpretable graph variational autoencoders X Guo, Y Du, S Tadepalli, L Zhao, A Shehu Bioinformatics Advances 1 (1), vbab036, 2021 | 43 | 2021 |
| Compact graph structure learning via mutual information compression N Liu, X Wang, L Wu, Y Chen, X Guo, C Shi Proceedings of the ACM web conference 2022, 1601-1610, 2022 | 40 | 2022 |
| Deep multi-attributed graph translation with node-edge co-evolution X Guo, L Zhao, C Nowzari, S Rafatirad, H Homayoun, SMP Dinakarrao 2019 IEEE International Conference on Data Mining (ICDM), 250-259, 2019 | 39 | 2019 |
| Deep generative model for periodic graphs S Wang, X Guo, L Zhao Advances in Neural Information Processing Systems 35, 35797-35810, 2022 | 35 | 2022 |
| Cognitive and scalable technique for securing IoT networks against malware epidemics SMP Dinakarrao, X Guo, H Sayadi, C Nowzari, A Sasan, S Rafatirad, ... IEEE Access 8, 138508-138528, 2020 | 35 | 2020 |
| Controllable data generation by deep learning: A review S Wang, Y Du, X Guo, B Pan, Z Qin, L Zhao arXiv preprint arXiv:2207.09542, 2022 | 34 | 2022 |
| Property controllable variational autoencoder via invertible mutual dependence X Guo, Y Du, L Zhao International Conference on Learning Representations, 2020 | 34 | 2020 |
| Distant-supervision of heterogeneous multitask learning for social event forecasting with multilingual indicators L Zhao, J Wang, X Guo Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 31 | 2018 |
| Deep generative models for spatial networks X Guo, Y Du, L Zhao Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 29 | 2021 |
| Disentangled spatiotemporal graph generative models Y Du, X Guo, H Cao, Y Ye, L Zhao Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6541-6549, 2022 | 28 | 2022 |
| Interpretable molecular graph generation via monotonic constraints Y Du, X Guo, A Shehu, L Zhao Proceedings of the 2022 SIAM International Conference on Data Mining (SDM …, 2022 | 27 | 2022 |
| Deep graph translation X Guo, L Wu, L Zhao IEEE Transactions on Neural Networks and Learning Systems 34 (11), 8225-8234, 2022 | 21 | 2022 |