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学者姓名：王石平
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Abstract ：
Traditional multiview clustering relies on manually-designed optimization problems based on prior interpretable knowledge to cluster objects with similar attributes, but it may be hindered by limited feature extraction capability. In contrast, deep multiview clustering overcomes this limitation by utilizing learning-based nonlinear transformations for clustering, but it may be restricted by model interpretability caused by blackbox networks. Besides, previous multiview clustering methods only consider homogeneous or heterogeneous situations, resulting in restricted scalability and generalizability. To address the aforementioned issues, we design a universal interpretable clustering framework that accommodates both homogeneous and heterogeneous multiview scenarios. To realize this purpose: 1) we revisit the interpretable knowledge-driven design architecture of traditional multiview methods and formulate clustering optimization problems on multiview data attributes in homogeneous scenarios; 2) the optimization problem is leveraged to derive network modules that learn shared and self-expressive representations for clustering, with the practical meaning of each network component providing model design-level interpretability; 3) the proposed method is extended from homogeneous to heterogeneous scenarios, enhancing its universality for a broader spectrum of multiview clustering tasks; and 4) tailored training loss for the clustering task is employed to inversely enhance the affinity between objects with similar attributes. Extensive experimental results on both homogeneous and heterogeneous multiview datasets demonstrate the superior effectiveness and adaptability of the proposed framework compared to state-of-the-art clustering methods.
Keyword ：
Clustering methods Clustering methods Computational modeling Computational modeling Data models Data models Feature extraction Feature extraction Homogeneity and heterogeneity learning Homogeneity and heterogeneity learning interpretable deep learning interpretable deep learning multiview clustering multiview clustering Noise Noise Optimization Optimization Semantics Semantics Social networking (online) Social networking (online) Topology Topology Training Training
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| GB/T 7714 | Wu, Chunming , Fang, Zihan , Du, Shide et al. A Universal Interpretable Multiview Clustering Framework: From Homogeneity to Heterogeneity [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2025 . |
| MLA | Wu, Chunming et al. "A Universal Interpretable Multiview Clustering Framework: From Homogeneity to Heterogeneity" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2025) . |
| APA | Wu, Chunming , Fang, Zihan , Du, Shide , Wang, Shiping . A Universal Interpretable Multiview Clustering Framework: From Homogeneity to Heterogeneity . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2025 . |
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In the dynamic environment of multimedia-sharing platforms like X (formerly known as Twitter) and TikTok, multimedia recommendation systems have been widely used to help users discover items of interest. However, traditional approaches often fall short, when the item modalities are incomplete, a common issue in realworld scenarios. To this end, we introduce the unified heterogeneous Hypergraph construction for the Incomplete multimedia REcommendation (HIRE), a novel framework designed to jointly learn a heterogeneous hypergraph and perform accurate recommendations under incomplete scenarios. HIRE first initializes the unified heterogeneous hypergraph for modality completion and employs self-supervised learning aligned with the contrastive text-centered view for multimedia recommendation. Such integration effectively handles the challenges posed by incomplete modalities, leading to improved recommendation accuracy. Furthermore, we find that the hypergraph directly learned from the HIRE is a dense structure which can be inaccurate and coarse. Therefore, we devise the HIRE framework with Sparse constraint named HIRES, which uniquely integrates optimal transport and a degrees 2,1-norm to refine the hypergraph structure. Our extensive experiments across various datasets demonstrate the superiority of HIRES in addressing incomplete modalities, establishing it as a powerful tool for personalized multimedia recommendations.
Keyword ：
Heterogeneous Hypergraph Heterogeneous Hypergraph Incomplete Multimedia Recommendation Incomplete Multimedia Recommendation Multimodal Representation Learning Multimodal Representation Learning Sparse Constraint Sparse Constraint
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| GB/T 7714 | Lin, Zhenghong , Tan, Yanchao , Chen, Jiamin et al. Unified Heterogeneous Hypergraph Construction for Incomplete Multimedia Recommendation [J]. | ACM TRANSACTIONS ON INFORMATION SYSTEMS , 2025 , 43 (5) . |
| MLA | Lin, Zhenghong et al. "Unified Heterogeneous Hypergraph Construction for Incomplete Multimedia Recommendation" . | ACM TRANSACTIONS ON INFORMATION SYSTEMS 43 . 5 (2025) . |
| APA | Lin, Zhenghong , Tan, Yanchao , Chen, Jiamin , Zhang, Hengyu , Chen, Chaochao , Wang, Shiping et al. Unified Heterogeneous Hypergraph Construction for Incomplete Multimedia Recommendation . | ACM TRANSACTIONS ON INFORMATION SYSTEMS , 2025 , 43 (5) . |
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The rapid evolution of multimedia technology has revolutionized human perception, paving the way for multi-view learning. However, traditional multi-view learning approaches are tailored for scenarios with fixed data views, falling short of emulating the intricate cognitive procedures of the human brain processing signals sequentially. Our cerebral architecture seamlessly integrates sequential data through intricate feed-forward and feedback mechanisms. In stark contrast, traditional methods struggle to generalize effectively when confronted with data spanning diverse domains, highlighting the need for innovative strategies that can mimic the brain's adaptability and dynamic integration capabilities. In this paper, we propose a bio-neurologically inspired multiview incremental framework named MVIL aimed at emulating the brain's fine-grained fusion of sequentially arriving views. MVIL lies two fundamental modules: structured Hebbian plasticity and synaptic partition learning. The structured Hebbian plasticity reshapes the structure of weights to express the high correlation between view representations, facilitating a fine-grained fusion of view representations. Moreover, synaptic partition learning is efficient in alleviating drastic changes in weights and also retaining old knowledge by inhibiting partial synapses. These modules bionically play a central role in reinforcing crucial associations between newly acquired information and existing knowledge repositories, thereby enhancing the network's capacity for generalization. Experimental results on six benchmark datasets show MVIL's effectiveness over state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Contrastive Learning Contrastive Learning Reinforcement learning Reinforcement learning
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| GB/T 7714 | Chen, Yuhong , Song, Ailin , Yin, Huifeng et al. Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency [C] . 2025 : 1265-1273 . |
| MLA | Chen, Yuhong et al. "Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency" . (2025) : 1265-1273 . |
| APA | Chen, Yuhong , Song, Ailin , Yin, Huifeng , Zhong, Shuai , Chen, Fuhai , Xu, Qi et al. Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency . (2025) : 1265-1273 . |
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Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained; 2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Deep learning Deep learning Multi-task learning Multi-task learning
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| GB/T 7714 | Du, Shide , Fang, Zihan , Tan, Yanchao et al. OpenViewer: Openness-Aware Multi-View Learning [C] . 2025 : 16389-16397 . |
| MLA | Du, Shide et al. "OpenViewer: Openness-Aware Multi-View Learning" . (2025) : 16389-16397 . |
| APA | Du, Shide , Fang, Zihan , Tan, Yanchao , Wang, Changwei , Wang, Shiping , Guo, Wenzhong . OpenViewer: Openness-Aware Multi-View Learning . (2025) : 16389-16397 . |
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In the context of Cyber Physical Social Intelligence (CPSI), efficiently training and inferring from samples with limited labels poses critical challenges due to the scarcity and high cost of label acquisition for big data. The aim is to attain high accuracy at minimal cost, thereby enhancing adaptation to the CPSI scenario. To tackle the challenges in CPSI, we present a multi-level feature learning framework for semi-supervised classification tasks. Initially, we employ a mapping operation for each view, extracting view-specific features with a feature-level reconstruction loss. These features are fused to obtain a shared feature. Simultaneously, a learnable graph neural network captures global topology using a graph structure-level reconstruction loss. Subsequently, a scalable graph convolution fusion module combines these features. Our evaluations on eight benchmark datasets show promising results and empirically prove the effectiveness of our approach, surpassing eight state-of-the-art methods in multi-view semi-supervised classification tasks. © 2018 Tsinghua University Press.
Keyword ：
Graph neural networks Graph neural networks Intelligent computing Intelligent computing Intelligent systems Intelligent systems Multi-task learning Multi-task learning Self-supervised learning Self-supervised learning Semi-supervised learning Semi-supervised learning
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| GB/T 7714 | Song, Na , Yang, Jing , Fu, Xuemei et al. Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence [J]. | Big Data Mining and Analytics , 2025 , 8 (4) : 837-850 . |
| MLA | Song, Na et al. "Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence" . | Big Data Mining and Analytics 8 . 4 (2025) : 837-850 . |
| APA | Song, Na , Yang, Jing , Fu, Xuemei , Yang, Xiangli , Xie, Ying , Wang, Shiping . Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence . | Big Data Mining and Analytics , 2025 , 8 (4) , 837-850 . |
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Active learning, as a technique, aims to effectively label specific data points while operating within a designated query budget. Nevertheless, the majority of unsupervised active learning algorithms are based on shallow linear representation and lack sufficient interpretability. Furthermore, certain diversity-based methods face challenges in selecting samples that adequately represent the entire data distribution. Inspired by these reasons, in this paper, we propose an unsupervised active learning method on orthogonal projections to construct a deep neural network model. By optimizing the orthogonal projection process, we establish the connection between projection and active learning, consequently enhancing the interpretability of the proposed method. The proposed method can efficiently project the feature space onto a spanned subspace, deriving an indicator matrix while calculating the projection loss. Moreover, we consider the redundancy among samples to ensure both data point diversity and enhancement of clustering-based algorithms. Through extensive comparative experiments on six public datasets, the results demonstrate that the proposed method can effectively select more informative and representative samples and improve performance by up to 11%.
Keyword ：
Active learning Active learning deep learning deep learning differentiable networks differentiable networks machine learning machine learning orthogonal projection orthogonal projection
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| GB/T 7714 | Pi, Yueyang , Shi, Yiqing , Du, Shide et al. Unsupervised Projected Sample Selector for Active Learning [J]. | IEEE TRANSACTIONS ON BIG DATA , 2025 , 11 (2) : 485-498 . |
| MLA | Pi, Yueyang et al. "Unsupervised Projected Sample Selector for Active Learning" . | IEEE TRANSACTIONS ON BIG DATA 11 . 2 (2025) : 485-498 . |
| APA | Pi, Yueyang , Shi, Yiqing , Du, Shide , Huang, Yang , Wang, Shiping . Unsupervised Projected Sample Selector for Active Learning . | IEEE TRANSACTIONS ON BIG DATA , 2025 , 11 (2) , 485-498 . |
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Asa promising area in machine learning, multi-view learning enhances model performance by integrating data from various views. With the rise of graph convolutional networks, many studies have explored incorporating them into multi-view learning frameworks. However, these methods often require storing the entire graph topology, leading to significant memory demands. Additionally, iterative update operations in graph convolutions lead to longer inference times, making it difficult to deploy existing multi-view learning models on large graphs. To overcome these challenges, we introduce an efficient multi-view graph convolutional network via local aggregation and global propagation. In the local aggregation module, we use a structure-aware matrix for feature aggregation, which significantly reduces computational complexity compared to traditional graph convolutions. After that, we design a global propagation module that allows the model to be trained in batches, enabling deployment on large-scale graphs. Finally, we introduce the attention mechanism into multi-view feature fusion to more effectively explore the consistency and complementarity between views. The proposed method is employed to perform multi-view semi-supervised classification, and comprehensive experimental results on benchmark datasets validate its effectiveness.
Keyword ：
Graph neural networks Graph neural networks Local aggregation Local aggregation Multi-view learning Multi-view learning Representation learning Representation learning Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Liu, Lu , Shi, Yongquan , Pi, Yueyang et al. Efficient multi-view graph convolutional networks via local aggregation and global propagation [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 266 . |
| MLA | Liu, Lu et al. "Efficient multi-view graph convolutional networks via local aggregation and global propagation" . | EXPERT SYSTEMS WITH APPLICATIONS 266 (2025) . |
| APA | Liu, Lu , Shi, Yongquan , Pi, Yueyang , Guo, Wenzhong , Wang, Shiping . Efficient multi-view graph convolutional networks via local aggregation and global propagation . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 266 . |
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Graph data presents a vast landscape for real-world applications. Current graph-level clustering approaches predominantly utilize graph neural networks to capture the intricate structural information for graph data. However, a significant challenge arises in effectively integrate structural and feature information under the prevalent noise in the real-world scenario. The advent of masking strategies has marked significant strides in boosting model robustness, accommodating incomplete data, and enhancing generalization capabilities. Yet, research attention on leveraging mask strategy for facilitating graph-level clustering is still limited. In this paper, we introduce a novel graph-level clustering method, towards adaptive masked structural learning for graph-level clustering. The method performs adaptive masking through reconstruction loss, and jointly adaptive mask representation learning and clustering in an end-to-end unsupervised framework. The mutual information between maximized the entire graph and substructure representations is also utilized to learn to generate cluster-oriented graph-level representations. Extensive experiments on eight real graph-level benchmark datasets demonstrate the effectiveness and superiority of the proposed method.
Keyword ：
Autoencoders Autoencoders Clustering methods Clustering methods Data science Data science deep learning deep learning Electronic mail Electronic mail Graph clustering Graph clustering graph neural networks graph neural networks Kernel Kernel Mutual information Mutual information Noise Noise Representation learning Representation learning Robustness Robustness Training Training unsupervised learning unsupervised learning
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| GB/T 7714 | Yang, Jinbin , Cai, Jinyu , Zhang, Yunhe et al. Towards Adaptive Masked Structural Learning for Graph-Level Clustering [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2025 , 12 (3) : 2021-2032 . |
| MLA | Yang, Jinbin et al. "Towards Adaptive Masked Structural Learning for Graph-Level Clustering" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 12 . 3 (2025) : 2021-2032 . |
| APA | Yang, Jinbin , Cai, Jinyu , Zhang, Yunhe , Huang, Sujia , Wang, Shiping . Towards Adaptive Masked Structural Learning for Graph-Level Clustering . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2025 , 12 (3) , 2021-2032 . |
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In practical applications, the difficulty of multi-view data annotation poses a challenge for multi-view semi-supervised learning. Although some graph-based approaches have been proposed for this task, they often struggle with capturing long-range information and memory bottlenecks, and usually encounter over-smoothing. To address these issues, this paper proposes an implicit model, named Multi-channel Equilibrium Graph Neural Network (MEGNN). Through an equilibrium point iterative process, the proposed MEGNN naturally captures long-range information and effectively reduces the consumption of memory compared with explicit models. Furthermore, the proposed method deals with the issue of over-smoothing in deep graph convolutional networks by residual connection and shrinkage factor. We analyze the effect of the shrinkage factor on the information capturing capability of the model, and demonstrate that the proposed method does not encounter over-smoothing. Comprehensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. © 1979-2012 IEEE.
Keyword ：
deep equilibrium model deep equilibrium model graph neural network graph neural network long-range dependency long-range dependency Multi-view learning Multi-view learning semi-supervised learning semi-supervised learning
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| GB/T 7714 | Wang, S. , Pi, Y. , Huang, Y. et al. Multi-Channel Equilibrium Graph Neural Network for Multi-View Semi-Supervised Learning [J]. | IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025 . |
| MLA | Wang, S. et al. "Multi-Channel Equilibrium Graph Neural Network for Multi-View Semi-Supervised Learning" . | IEEE Transactions on Pattern Analysis and Machine Intelligence (2025) . |
| APA | Wang, S. , Pi, Y. , Huang, Y. , Chen, F. , Zhang, L. . Multi-Channel Equilibrium Graph Neural Network for Multi-View Semi-Supervised Learning . | IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025 . |
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The utilization of H2S from industrial by-products as a hydrogen source offers an alternative approach for converting nitroarenes compounds (Ph-NO2) into aromatic amine (Ph-NH2) while also mitigating this environmental pollutant. However, the progress of this technology has been hindered by the lack of cost-effective and efficient catalysts. Herein, we present a porous K2MoSx/SiO2 catalyst synthesized using low-cost and environmentally friendly proline as template. The optimized K2MoSx/SiO2-proline catalyst achieves an 84 % conversion of Ph-NO2 with high Ph-NH2 selectivity for 91 % in the catalytic reduction of Ph-NO2 with H2S. Additionally, the K2MoSx/SiO2-proline shows considerable catalytic activity for the reduction of various substituted nitroarenes, demonstrating its versatility and broad applicability. The in situ DRIFTS and DFT calculations indicate that the reaction favors the formation of the essential intermediate *Ph-NO through a single H-induced pathway before proceeding to hydrogenation to yield Ph-NH2.
Keyword ：
Aromatic amine Aromatic amine H2S utilization H2S utilization Nitroarenes hydrogenation Nitroarenes hydrogenation Porous SiO2 Porous SiO2 Proline templates Proline templates
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| GB/T 7714 | Huang, Rui , Jiang, Weiping , Zheng, Xiaohai et al. Harnessing waste H2S for aromatic amines production over porous K2MoSX/SiO2 catalyst [J]. | CHEMICAL ENGINEERING JOURNAL , 2025 , 511 . |
| MLA | Huang, Rui et al. "Harnessing waste H2S for aromatic amines production over porous K2MoSX/SiO2 catalyst" . | CHEMICAL ENGINEERING JOURNAL 511 (2025) . |
| APA | Huang, Rui , Jiang, Weiping , Zheng, Xiaohai , Lei, Ganchang , Wang, Shiping , Liang, Shijing et al. Harnessing waste H2S for aromatic amines production over porous K2MoSX/SiO2 catalyst . | CHEMICAL ENGINEERING JOURNAL , 2025 , 511 . |
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