代表性工作

[1] W. Tu, S. Zhou, X. Liu, Z. Cai, Y. Zhao, Y. Liu, and K. He. WAGE: Weight-Sharing Attribute-Missing Graph Autoencoder. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 47(7): 5760-5777, 2025. [CCF A]

[2] W. Tu, S. Zhou, X. Liu, X. Guo, Z. Cai, E. Zhu, and J. Cheng. Deep Fusion Clustering Network. Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021, pages: 9978-9987. [CCF A]

[3] W. Tu, R. Guan, S. Zhou, C. Ma, X. Peng, Z. Cai, Z. Liu, J. Cheng, and X. Liu. Attribute-Missing Graph Clustering Network. Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI), 2024, pages: 15392-15401. [CCF A]

[4] W. Tu, S. Zhou, X. Liu, Y. Liu, Z. Cai, E. Zhu, C. Zhang, and J. Cheng. Initializing Then Refining: A Simple Graph Attribute Imputation Network. Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI), 2022, pages: 3494-3500. [CCF A]

[5] W. Tu, Q. Liao, S. Zhou, X. Peng, C. Ma, Z. Liu, X. Liu, Z. Cai, and K. He. RARE: Robust Masked Graph Autoencoder. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 36(10): 5340-5353, 2024. [CCF A]

[6] W. Tu, S. Zhou, X. Liu, C. Ge, Z. Cai, and Y. Liu. Hierarchically Contrastive Hard Sample Mining for Graph Self-Supervised Pretraining. IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 35(11): 16748-16761, 2024. [中科院1区Top]

[7] W. Tu, B. Xiao, X. Liu, S. Zhou, Z. Cai, and J. Chen. Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network. IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 36(2): 3244-3257, 2025. [中科院1区Top]

[8] R. Guan, W. Tu*, D. Hu, W. Liang, K. Liang, Y. Hu, Y. Liu, and X. Liu. Prototype-Driven Multi-View Attribute-Missing Graph Clustering. IEEE Transactions on Multimedia (TMM), 27: 9454-9466, 2025. [中科院1区Top]

[9] J. Wang, W. Tu*, and Jieren Cheng. Hierarchical Shortest-Path Graph Kernel Network. Proceedings of the 39th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. [CCF A]

[10]  L. Wang, W. Tu*, J. Wang, X. Wang, J. Cheng, and J. Liu. Federated Invariant Graph Learning for Non-IID Graphs. Proceedings of the 39th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. [CCF A]

[11]  J. Wang, W. Tu*, L. Wang, J. Cheng, and Y. Yang. Anchor-Driven Nyström for Deep Graph-Level Clustering. Proceedings of the 40th AAAI Conference on Artificial Intelligence (AAAI), 2026, pages: 26260-26268. [CCF A]

[12]  L. Wang, W. Tu*, J. Cheng, J. Wang, X. Tang, and C. Wang. Discovering Maximum Frequency Consensus: Lightweight Federated Learning for Medical Image Segmentation. Proceedings of the 33rd ACM International Conference on Multimedia (ACM MM), 2025, pages: 1900-1909. [CCF A]

[13]  J. Wu, R. Han, W. Tu*, J. Liu, H. Wang, and J. Cheng. FedCND: Federated Graph-Level Clustering under Inter-Client Cluster Number Discrepancy. Proceedings of the 35th ACM on Web Conference (WWW), 2026. [CCF A]

[14]  J. Liu, R. Han, W. Tu*, H. Wang, J. Wu, and J. Cheng. Federated Node-Level Clustering Network with Cross-Subgraph Link Mending. Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025, pages: 38540-38556. [CCF A]

[15]  M. Tao, Y. Tian, W. Tu*, Y. Yang, X. Yang, and X. Tang. Safe-FedLLM: Delving into the Safety of Federated Large Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL, Main Conference), 2026. [CCF A]