| Review of bankruptcy prediction using machine learning and deep learning techniques Y Qu, P Quan, M Lei, Y Shi Procedia Computer Science 162, 895-899, 2019 | 143 | 2019 |
| Diffusion network embedding Y Shi, M Lei, H Yang, L Niu Pattern Recognition 88, 518-531, 2019 | 36 | 2019 |
| FC–HAT: hypergraph attention network for functional brain network classification J Ji, Y Ren, M Lei Information Sciences 608, 1301-1316, 2022 | 35 | 2022 |
| Distant supervision relation extraction via adaptive dependency-path and additional knowledge graph supervision Y Shi, Y Xiao, P Quan, M Lei, L Niu Neural Networks 134, 42-53, 2021 | 35 | 2021 |
| Self-supervised spatiotemporal graph neural networks with self-distillation for traffic prediction J Ji, F Yu, M Lei IEEE Transactions on Intelligent Transportation Systems 24 (2), 1580-1593, 2022 | 21 | 2022 |
| Exploring brain effective connectivity networks through spatiotemporal graph convolutional models A Zou, J Ji, M Lei, J Liu, Y Song IEEE Transactions on Neural Networks and Learning Systems, 2022 | 20 | 2022 |
| Document-level relation extraction via graph transformer networks and temporal convolutional networks Y Shi, Y Xiao, P Quan, ML Lei, L Niu Pattern Recognition Letters 149, 150-156, 2021 | 20 | 2021 |
| Relation constraint self-attention for image captioning J Ji, M Wang, X Zhang, M Lei, L Qu Neurocomputing 501, 778-789, 2022 | 16 | 2022 |
| A brief review of receptive fields in graph convolutional networks P Quan, Y Shi, M Lei, J Leng, T Zhang, L Niu IEEE/WIC/ACM International Conference on Web Intelligence-Companion Volume …, 2019 | 15 | 2019 |
| Supervised contrastive learning with structure inference for graph classification J Ji, H Jia, Y Ren, M Lei IEEE Transactions on Network Science and Engineering 10 (3), 1684-1695, 2023 | 13 | 2023 |
| DigGCN: Learning compact graph convolutional networks via diffusion aggregation M Lei, P Quan, R Ma, Y Shi, L Niu IEEE Transactions on Cybernetics 52 (2), 912-924, 2020 | 13 | 2020 |
| A brief survey for fake news detection via deep learning models J Li, M Lei Procedia Computer Science 214, 1339-1344, 2022 | 11 | 2022 |
| Self-supervised knowledge distillation for complementary label learning J Liu, B Li, M Lei, Y Shi Neural Networks 155, 318-327, 2022 | 10 | 2022 |
| Discrete embedding for latent networks H Yang, L Chen, M Lei, L Niu, C Zhou, P Zhang International Joint Conference on Artificial Intelligence, 2021 | 9 | 2021 |
| Graph influence network Y Shi, P Quan, Y Xiao, M Lei, L Niu IEEE Transactions on Cybernetics 53 (10), 6146-6159, 2022 | 8 | 2022 |
| Multi-task self-distillation for graph-based semi-supervised learning Y Ren, J Ji, L Niu, M Lei arXiv preprint arXiv:2112.01174, 2021 | 8 | 2021 |
| Latent neighborhood-based heterogeneous graph representation Y Xiao, P Quan, ML Lei, L Niu Neural Networks 154, 413-424, 2022 | 7 | 2022 |
| Optimization strategies in quantized neural networks: A review Y Lin, M Lei, L Niu 2019 International Conference on Data Mining Workshops (ICDMW), 385-390, 2019 | 7 | 2019 |
| Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation J Ji, Y Liang, M Lei Neural Networks 142, 522-533, 2021 | 6 | 2021 |
| Spatio-Temporal Transformer Network for Weather Forecasting J Ji, J He, M Lei, M Wang, W Tang IEEE Transactions on Big Data, 2024 | 4 | 2024 |
