Scalable multi-view subspace clustering with unified anchors M Sun, P Zhang, S Wang, S Zhou, W Tu, X Liu, E Zhu, C Wang Proceedings of the 29th ACM international conference on multimedia, 3528-3536, 2021 | 217 | 2021 |
Deep graph clustering via dual correlation reduction Y Liu, W Tu, S Zhou, X Liu, L Song, X Yang, E Zhu Proceedings of the AAAI conference on artificial intelligence 36 (7), 7603-7611, 2022 | 208 | 2022 |
Deep fusion clustering network W Tu, S Zhou, X Liu, X Guo, Z Cai, E Zhu, J Cheng Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 9978-9987, 2021 | 189 | 2021 |
Simple contrastive graph clustering Y Liu, X Yang, S Zhou, X Liu, S Wang, K Liang, W Tu, L Li IEEE Transactions on Neural Networks and Learning Systems, 2023 | 134 | 2023 |
Highly-efficient incomplete large-scale multi-view clustering with consensus bipartite graph S Wang, X Liu, L Liu, W Tu, X Zhu, J Liu, S Zhou, E Zhu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 131 | 2022 |
One pass late fusion multi-view clustering X Liu, L Liu, Q Liao, S Wang, Y Zhang, W Tu, C Tang, J Liu, E Zhu International conference on machine learning, 6850-6859, 2021 | 117 | 2021 |
Hard sample aware network for contrastive deep graph clustering Y Liu, X Yang, S Zhou, X Liu, Z Wang, K Liang, W Tu, L Li, J Duan, ... Proceedings of the AAAI conference on artificial intelligence 37 (7), 8914-8922, 2023 | 116 | 2023 |
Survey on graph convolutional neural network J LIU, W TU, E ZHU Computer engineering & science 45 (08), 1472, 2023 | 113 | 2023 |
A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal K Liang, L Meng, M Liu, Y Liu, W Tu, S Wang, S Zhou, X Liu, F Sun, K He IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | 104 | 2024 |
Cluster-guided contrastive graph clustering network X Yang, Y Liu, S Zhou, S Wang, W Tu, Q Zheng, X Liu, L Fang, E Zhu Proceedings of the AAAI conference on artificial intelligence 37 (9), 10834 …, 2023 | 100 | 2023 |
Reasoning over different types of knowledge graphs: Static, temporal and multi-modal K Liang, L Meng, M Liu, Y Liu, W Tu, S Wang, S Zhou, X Liu, F Sun arXiv preprint arXiv:2212.05767 3, 2022 | 88 | 2022 |
Knowledge graph contrastive learning based on relation-symmetrical structure K Liang, Y Liu, S Zhou, W Tu, Y Wen, X Yang, X Dong, X Liu IEEE Transactions on Knowledge and Data Engineering 36 (1), 226-238, 2023 | 85 | 2023 |
Self-representation subspace clustering for incomplete multi-view data J Liu, X Liu, Y Zhang, P Zhang, W Tu, S Wang, S Zhou, W Liang, S Wang, ... Proceedings of the 29th ACM international conference on multimedia, 2726-2734, 2021 | 85 | 2021 |
Learn from relational correlations and periodic events for temporal knowledge graph reasoning K Liang, L Meng, M Liu, Y Liu, W Tu, S Wang, S Zhou, X Liu Proceedings of the 46th international ACM SIGIR conference on research and …, 2023 | 78 | 2023 |
Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences S Wang, X Liu, S Liu, J Jin, W Tu, X Zhu, E Zhu Advances in Neural Information Processing Systems 35, 5882-5895, 2022 | 66 | 2022 |
Deep temporal graph clustering M Liu, Y Liu, K Liang, W Tu, S Wang, S Zhou, X Liu arXiv preprint arXiv:2305.10738, 2023 | 62 | 2023 |
Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks R Guan, Z Li, W Tu, J Wang, Y Liu, X Li, C Tang, R Feng IEEE Transactions on Geoscience and Remote Sensing, 2024 | 57 | 2024 |
Attributed Graph Clustering with Dual Redundancy Reduction. L Gong, S Zhou, W Tu, X Liu IJCAI, 3015-3021, 2022 | 57 | 2022 |
Self-supervised temporal graph learning with temporal and structural intensity alignment M Liu, K Liang, Y Zhao, W Tu, S Zhou, X Gan, X Liu, K He IEEE Transactions on Neural Networks and Learning Systems, 2024 | 55 | 2024 |
DFFNet: An IoT-perceptive dual feature fusion network for general real-time semantic segmentation X Tang, W Tu, K Li, J Cheng Information Sciences 565, 326-343, 2021 | 49 | 2021 |