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Preprints

• Unity in Diversity: Consensus-Structured Specificity Learning for Incomplete Multi-View Clustering, 2026. Preprint, prepared by Yu Cao. (通讯)

• Anchor-Sample Centroid Alignment for Large-scale Multi-view Subspace Clustering, 2026. Preprint, prepared by Xiang Xu. (通讯)

• Collaborative-Aware Representation Augmentation and Dual-Channel Adaptive Contrastive Learning for Multi-View Graph Clustering, 2025. Preprint, prepared by Tianxiang Zhao. (通讯)

• Global-and-Local Mixture-of-Experts with Dual-level Contrastive Learning for Deep Multi-view Clustering, 2025. Preprint, prepared by Bin Xiao. (通讯)

• Dual-guard Continual Dictionary Learning with Adaptive Atom Replacement and Principal Subspace Preservation for Incremental Multi-mode Process Monitoring, 2025. Preprint, prepared by Yan Pan. (通讯)

• Cross-mode Reconstruction and Diverse Low-rank Embedding Representation Learning for Multi-mode Process Monitoring, 2025. Preprint, prepared by Yan Pan. (通讯) (Major Revision)

• Heterogeneity is Not Global Metric: Mixed-pass Aggregation via Parallel Cross-filtering for GNNs, 2025. Preprint, prepared by Jiahao Long. (通讯)

• Decoupling then Soft Cross-view Association induced Graph Representation Learning, 2026. Under review, prepared by Jinlu Wang. （通讯）

• Sparse Matrix Estimation for Dynamic Process Monitoring based on Multiperiod Bipartite Graph Embedding Learning, 2025. Under review, prepared by Mingliang Cui. (三作)

• Grassmann Manifold Trajectory Representation for Video Domain Adaptation, 2025. Under review, prepared by Jinlu Wang. （通讯）

• Joint tensor self-representation and discriminative feature extraction for multi-view clustering, 2025. Under review, prepared by Fen Xu. (三作) (Major Revision)

• ParetoBoost: A Conflict-Averse Gradient Fusion Framework for Imbalanced Ensemble Learning, 2026. Under review, prepared by Ye Su. (三作)

• From Bias Decay to Variance Control: Gradient-Guided Newton Boosting, 2026. Under review, prepared by Ye Su. (四作)

• A novel ensemble learning algorithm with bagging-boosting hybrid fusion architecture: towards imbalanced classification and interpretable feature selection, 2026. Under review, prepared by Ye Su. (三作, 共同通讯)

• VIBoost: Re-framing Boosting Paradigm from a Variational Inference Perspective, 2026. Under review, prepared by Ye Su. (三作)

• Aligning What with When: Knowledge Graph–Grounded Retrieval and Temporal Adaptation for LLM Reasoning, 2026. Under review, prepared by Jiapu Wang. (五作)

• Exact Finite-Sample Variance Decomposition of Subagging: A Spectral Filtering Perspective, 2026. [PDF] Under review, prepared by Ye Su. (四作)

Publications (* indicating the corresponding author)

[2026]

• Youqing Wang, TianxiangZhao, Mengyuan Xin, Ye Su, Jiapu Wang, Tengfei Liu, Junbin Gao, Jipeng Guo*. “Deep Multi-view Graph Clustering via Attribute-aware Bidirectional Structural Refinement and Pseudo-label Guided Multi-level Fusion”, ICML, 2026.

• Youqing Wang, Jiahao Long, TianxiangZhao*, Man Cao, Mengyuan Xin, Jiapu Wang, Junbin Gao, Jipeng Guo*. “Dual-channel Dynamic Graph Neural Networks with Adaptive Adjacency Learning and Multi-scale Representation Fusion”, ICML, 2026.

• Ye Su, Longlong Zhao*, Diego García-Gil, Jipeng Guo, Gangchun Zhang, Jinxin Chen, Jinsong Chen. “ITBoost: Information-Theoretic Trust for Robust Boosting”, IJCAI, 2026.

• Tianqi Zheng, Xiangcheng Li, Youming Sun, Jipeng Guo, Tianchuan Yang, Haiqiang Chen. “Feature Selection and Double Self-expressive Tensor Fusion for Multi-view Subspace Clustering”, Information Sciences, 2026. [PDF] [Code]

• Jinlu Wang, Yanfeng Sun, Junbin Gao, Shaofan Wang, Qi Zhang, Yachao Yang, Baocai Yin, Jipeng Guo*. “Hybrid Attention Learning with Pseudo-label Prompted Adaptive Graph Evolution”, Neural Networks, 2026. (通讯) [PDF]

• Yachao Yang, Yanfeng Sun*, Jipeng Guo*, Shaofan Wang, Jinlu Wang, Junbin Gao, Fujiao Fu, Baocai Yin. “Attribute-Topology Cross-Frequency Aligned Graph Neural Networks for Homophilic and Heterophilic Graphs”, IEEE Transactions on Neural Networks and Learning Systems, 2026. [PDF] (共同通讯)

• Yi Liu (本科生), Tianruo Liu, Tianxiang Zhao, Weijie Shi, Jipeng Guo*, Feiyao Yu. “From Local to Global: Leveraging Graph Transformer and Graph Convolutional Networks for Heterophilic Graphs”, IJCNN, 2026. [Code]

• Tianyou Yu† (本科生), Kailin Zhang† (本科生), Jianlin Zheng† (本科生), Tianruo Liu, Tianxiang Zhao*, Haopeng Yang, Jipeng Guo*. “Multi-order Filter Fusion and Triple Contrastive Learning for Attribute Graph Clustering”, IJCNN, 2026. (共同通讯, †共同一作) [Code]

• Jipeng Guo, Xiang Xu, Yu Cao, Man Cao, Mengyuan Xin, Tianxiang Zhao, Ye Su, Junbin Gao, Mingliang Cui, Youqing Wang*. “Anchor-to-Graph Structural Co-regularization for Scalable Multi-View Clustering”, Pattern Recognition, 2026, 177: 113378. [PDF] [Code]

• Jinlu Wang, Yanfeng Sun, Jiapu Wang, Junbin Gao, Shaofan Wang, Baocai Yin, Jipeng Guo*. “ComGRL: Comprehensive Graph Representation Learning from Local to Global Bridged by Mixup”, IEEE Transactions on Computational Social Systems, 2026. [PDF] [Code]

• Youqing Wang, Yu Cao, Jinlu Wang, Xiang Xu, Jiapu Wang, Tengfei Liu, Junbin Gao, Jipeng Guo*. “Aligning Collaborative View Recovery and Tensorial Subspace Learning via Latent Representation for Incomplete Multi-View Clustering”, ICLR, 2026. [PDF] [Code]

• Haoyan Yang, Qianyin Wei, Tianchuan Yang, Jipeng Guo*. “Adaptive Sparse Graph for Multi-view Clustering”, Digital Signal Processing, 2026. [PDF] [Code]

• 王友清, 徐世龙, 赵天祥, 王宇晨, 辛梦媛, 张琦, 苏烨, 郭继鹏*. “基于分数阶图神经扩散的跨频域对齐对比学习方法”, 自动化学报, 2026. (CCF-A) [PDF] [Code] (doi: 10.16383/j.aas.c250604)

• Tianxiang Zhao, Youqing Wang, Shilong Xu, Tianchuan Yang Junbin Gao, Jipeng Guo*. “Dual-level Noise Augmentation for Graph Clustering with Triplet-wise Contrastive Learning”, Pattern Recognition, 2026, 172: 112463. [PDF] [Code]

• Youqing Wang, Tianxiang Zhao, Mingliang Cui, Junbin Gao, Li Liang, Jipeng Guo*. “Representation then Augmentation: Wide Graph Clustering Network with Multi-order Filter Fusion and Double-level Contrastive Learning”, IEEE/CAA Journal of Automatica Sinica, 2026, 13(2): 421-435. [PDF] [Code]

• 朱生刚, 杨皓鹏, 李昱翰, 刘明佳, 郭继鹏, 王友清*. “基于全局-局部结构保持的鲁棒非负矩阵分解故障检测方法”, 控制工程, 2026.

[2025]

• Tianxiang Zhao, Youqing Wang, Jinlu Wang, Jiapu Wang, Mingliang Cui, Junbin Gao, Jipeng Guo*. “Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clustering”, NeurIPS, 2025. [Code]

• Youqing Wang, Tengxiao Yin, Shenggang Zhu, Xin Ma, Li Liang, Jipeng Guo*. “FIGNN: Fuzzy Inference-guided Graph Neural Network for Fault Diagnosis in Industrial Processes”, IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3542316. [PDF] [Code]

• Tianchuan Yang, Chang-Dong Wang, Jipeng Guo, Xiangcheng Li, Man-Sheng Chen, Shuping Dang, Haiqiang Chen. “Triplets-based Large-scale Multi-view Spectral Clustering”,Information Fusion, 2025, 121: 103134. [PDF] [Code]

• Jipeng Guo, Yanfeng Sun, Xin Ma, Junbin Gao, Yongli Hu, Youqing Wang*, and Baocai Yin. “Globality Meets Locality: an Anchor Graph Collaborative Learning Framework for Fast Multi-view Subspace Clustering”, IEEE Transactions on Neural Networks and Learning System, 2025, 36(6): 10213-10227. [PDF]

• Mingliang Cui, Youqing Wang, Jipeng Guo, Tongze Hou, and Xin Ma*. “A Dynamic Process Modeling Method based on Bipartite Graph and Recursive Monitoring for Catalytic Cracking Unit”, IEEE Transactions on Control Systems Technology, 2025. [PDF]

• Mingliang Cui, Xin Ma, Youqing Wang*, Jipeng Guo, and Tongze Hou. “Optimal Sparse Principal Component Analysis with A Varying Hyperparameter for Industrial Fault Diagnosis”, IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3506413. [PDF]

• 郭继鹏, 徐世龙, 龙家豪, 王友清*, 孙艳丰, 尹宝才. “基于双跨视相关性检测的多视子空间聚类”, 计算机工程, 2025, 51(4): 27-36. [PDF] (CCF T2类)

• Jinlu Wang, Jipeng Guo*, Yanfeng Sun, Junbin Gao, Shaofan Wang, Yachao Yang, and Baocai Yin. “DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations”, IEEE Transactions on Big Data, 2025, 11(4): 1813-1827. [PDF] [Code]

• Mingliang Cui, Xin Ma, Youqing Wang*, Jipeng Guo, and Tongze Hou. “Fast Sparse Dynamic Matrix Estimation Method with Differential Information for Industrial Process Monitoring”, IEEE Transactions on Control Systems Technology, 2025, 33(2): 512-525. [PDF]

• Shuchang Guo (本科生), Wenxuan Chen (本科生), Junbin Gao, Shilong Xu, Yuhan Li, Mingjia Liu, Jipeng Guo*, Youqing Wang. “Decouple then Fusion: Flexible Graph Representation Learning with Cross-frequency Diversity”, In Proceedings of International Joint Conference on Neural Networks (IJCNN), 2025. (CCF-C)

• Tengxiao Yin, Yuchen Wang (本科生), Yuhan Li, Mingjia Liu, Jipeng Guo*, Wouqing Wang. “Fuzzy Inference and Temporal Logic based Reliable Graph Neural Network for Industrial Fault Diagnosis”, In Proceedings of the Youth Academie Annual Conference of Chinese (YAC), 2025.

• Yan Pan, Jipeng Guo, Haopeng Yang (本科生), Yuchen Wang (本科生), Wenxuan Chen (本科生), Youqing Wang. “Joint Low-rank and Sparse Double Dictionary Learning for Industrial Process Monitoring”, In Proceedings of The Unified International Conference on Emerging Technologies in Cyber-Physical Systems and Industrial AI, 2025, 939-951. [PDF]

[2024]

• Jipeng Guo, Tengxiao Yin, Tianxiang Zhao, Jiayi Zhao, Yanfeng Sun, Junbin Gao, and Youqing Wang*. “Improved attributed graph clustering with representation and structure augmentation”, In Proceedings of International Joint Conference on Neural Networks (IJCNN), 2024. (CCF-C) [PDF]

• Yachao Yang, Yanfeng Sun*, Shaofan Wang, Jipeng Guo, Junbin Gao, Fujiao Ju, and Baocai Yin. “Graph Neural Networks with Soft Association between Topology and Attribute”, In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024, 9260-9268. (Oral, CCF-A) [PDF] [Code]

• Yachao Yang, Jipeng Guo, Yanfeng Sun*, Shaofan Wang, Junbin Gao, and Baocai Yin. “Multi-graph Fusion and Virtual Node Enhanced Graph Neural Networks”, In Proceedings of 33rd International Conference on Artificial Neural Networks (ICANN), 2024. (CCF-C) [PDF]

• Qi Zhang, Yanfeng Sun*, Jipeng Guo, Shaofan Wang, Jinghua Li, Junbin Gao, and Baocai Yin. “AUTOFGNN: A Framework for Extracting All Frequency Information from Large-scale Graphs”, In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024. (CCF-B) [PDF]

• Jiahao Long, Youqing Wang*, Jipeng Guo, Shilong Xu, and Li Liang. “Topological-Semantic Structure and Attribute Representations Decoupling Learning for Improved Graph Neural Networks”, In Proceedings of Chinese Automation Congress (CAC), 2024, 2523-2528. [PDF] [Code]

• Shilong Xu, Jipeng Guo, Jiahao Long, Mingliang Cui, and Youqing Wang*. “High-Low Frequency Filtering aware Graph Neural Networks with Representation Decoupling Learning”, In Proceedings of Chinese Automation Congress (CAC), 2024, 4005-4010. [PDF] [Code]

• Bin Xiao, Jipeng Guo, Juntao Hu, Yifan Dong, and Youqing Wang*. “Comprehensive Multi-view Subspace Clustering with Global-and-Local Representation Learning”, In Proceedings of The 17th Annual International Conference on Combinatorial Optimization and Applications (COCOA), 2024. (Accepted)

[2023]

• Jipeng Guo, Yanfeng Sun*, Junbin Gao, Yongli Hu, and Baocai Yin. “Logarithmic Schatten-p Norm Minimization for Tensorial Multi-view Subspace Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023, 45(3): 3396-3410. [PDF]

• Jipeng Guo, Yanfeng Sun*, Junbin Gao, Yongli Hu, and Baocai Yin. “Robust discriminant analysis with feature selective projection and between-classes structural incoherence”, Digital Signal Processing, 2023, 134: 103896. [PDF]

[2022]

• Jipeng Guo, Yanfeng Sun*, Junbin Gao, Yongli Hu, and Baocai Yin. “Multi-attribute Subspace Clustering via Auto-weighted Tensor Nuclear Norm Minimization”, IEEE Transactions on Image Processing (TIP), 2022, 31: 7191-7205. [PDF]

• Jipeng Guo, Yanfeng Sun*, Junbin Gao, Yongli Hu, and Baocai Yin. “Rank Consistency Induced Multi-view Subspace Clustering via Low-rank Matrix Factorization”, IEEE Transactions on Neural Networks and Learning System (TNNLS), 2022, 33(7): 3157-3170. [PDF] [Poster]

• Jiyi Zhao#, Jipeng Guo#, Yanfeng Sun*, Junbin Gao, Yongli Hu, and Baocai Yin. “Adaptive Graph Convolutional Clustering Network with Optimal Probabilistic Graph”, Neural Networks (NN), 2022, 156: 271-284. (#共同一作) [PDF]

• Yanfeng Sun*, Jie Wang, Jipeng Guo, Yongli Hu, and Baocai Yin. “Globality constrained adaptive graph regularized non‐negative matrix factorization for data representation”, IET Image Processing, 2022, 16(10): 2577-2592. [PDF]

• Jiayi Zhao, Yanfeng Sun*, Jipeng Guo, Junbin Gao, and Baocai Yin. “Robust graph convolutional clustering with adaptive graph learning”, In Proceedings of International Joint Conference on Neural Networks (IJCNN), 2022, 1-8. (CCF-C) [PDF]

[2021]

• Jipeng Guo, Yanfeng Sun*, Junbin Gao, Yongli Hu, and Baocai Yin. “Low-rank Representation on Product Grassmann Manifolds for Multi-view Subspace Clustering”, In Proceedings of International Conference on Pattern Recognition (ICPR), 2021, 907-914. (CCF-C) [PDF]

• Jipeng Guo, Shuai Yin, Yanfeng Sun*, and Yongli Hu. “Double Manifolds Regularized Non-negative Matrix Factorization for Data Representation”, In Proceedings of International Conference on Pattern Recognition (ICPR), 2021, 901-906. (CCF-C) [PDF]

• Jie Wang, Yanfeng Sun*, Jipeng Guo, Yongli Hu, and Baocai Yin. “Attributed Non-negative Matrix Multi-factorization for Data Representation”, In Proceedings of Pattern Recognition and Computer Vision: 4th Chinese Conference (PRCV), 2021, 66-77. (CCF-C) [PDF]

[2020]

• Jipeng Guo, Yanfeng Sun*, Junbin Gao, Yongli Hu, and Baocai Yin. “Robust Adaptive Linear Discriminant Analysis with Bidirectional Reconstruction Constraint”, ACM Transactions on Knowledge Discovery from Data (TKDD), 2020, 14(6): 75. [PDF]