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刘景欣 的个人主页
  • 刘景欣
  1. 职  务:博士后
  2. 学  院:J9旗舰厅
  3. 学历职称:博士
  4. 联系方式:ljx_0417@hainanu.edu.cn
个人简介 发表论文 发明专利

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

[2] J. Liu, X. Tang, R. Han, W. Tu, R. Wang. Adaptive Feature Boosting and Distribution Refinement for Graph Clustering [J]. Pattern Recognition (PR), 2025, pages: 112309-112320. [中科院1Top]

[3] J. Liu, W. Tu, R. Han, J. Wu, H. Wang, G. Liu, X. Tang, Y. Yang. Personalized Federated Graph-Level Clustering Network [C]. Proceedings of the 40th AAAI Conference on Artificial Intelligence (AAAI), 2026, pages:23721-23729. [CCF A]

[4] J. Liu, J. Cheng, R. Han, W. Tu, J. Wang, X. Peng. Federated Graph-Level Clustering Network [C]. Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI), 2025, pages: 18870-18878. [CCF A]

[5] J. Liu, W. Tu, H. Wang, R. Han, J. Wu, X. Tang. Causally-Aware Attribute Completion for Incomplete Federated Graph Clustering [C]. Proceedings of the 40th AAAI Conference on Artificial Intelligence (AAAI), 2026, pages: 23730-23738. [CCF A]

[6] X. Tang, J. Liu*, K. Li, W. Tu, X. Xu, N. Xiong. IIM-ARE: An Effective Interactive Incentive Mechanism Based on Adaptive Reputation Evaluation for Mobile Crowd Sensing [J]. IEEE Internet of Things Journal (IoTJ), 2025, pages: 16181-16191. [中科院1Top]

[7] R. Han, J. Wu, W. Tu, J. Liu, H. Wang, J. Cheng. Federated Graph-level Clustering Network with Attribute Inference [C]. Proceedings of the 40th AAAI Conference on Artificial Intelligence (AAAI), 2026, pages: 21585-21593. [CCF A]

[8] L. Wang, W. Tu, J. Wang, X. Wang, J. Cheng, J. Liu. FedIGL: Federated Invariant Graph Learning for Non-IID Graphs [C]. Proceedings of the 39th Conference on Neural Information Processing Systems (NeurIPS), 2025, pages: 117917-117940. [CCF A]

[9] J. Wu, R. Han, W. Tu, J. Liu, H. Wang, J. Cheng. FedCND: Federated Graph-Level Clustering under Inter-Client Cluster Number Discrepancy [C]. Proceedings of the ACM Web Conference (WWW), 2026, pages: 925-934. [CCF A]

[10] X. Peng, J. Cheng, X. Tang, J. Liu, J. Wu. Dual contrastive learning network for graph clustering [J]. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023, pages: 10846-10856. [中科院1Top]



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