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学者姓名：王石平
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Abstract ：
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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Recent studies highlight the growing appeal of multi-view learning due to its enhanced generalization. Semi-supervised classification, using few labeled samples to classify the unlabeled majority, is gaining popularity for its time and cost efficiency, particularly with high-dimensional and large-scale multi-view data. Existing graph-based methods for multi-view semi-supervised classification still have potential for improvement in further enhancing classification accuracy. Since deep random walk has demonstrated promising performance across diverse fields and shows potential for semi-supervised classification. This paper proposes a deep random walk inspired multi-view graph convolutional network model for semi-supervised classification tasks that builds signal propagation between connected vertices of the graph based on transfer probabilities. The learned representation matrices from different views are fused by an aggregator to learn appropriate weights, which are then normalized for label prediction. The proposed method partially reduces overfitting, and comprehensive experiments show it delivers impressive performance compared to other state-of-the-art algorithms, with classification accuracy improving by more than 5% on certain test datasets.
Keyword ：
Deep random walk Deep random walk Graph convolutional networks Graph convolutional networks Multi-view learning Multi-view learning Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Chen, Zexi , Chen, Weibin , Yao, Jie et al. Deep random walk inspired multi-view graph convolutional networks for semi-supervised classification [J]. | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
| MLA | Chen, Zexi et al. "Deep random walk inspired multi-view graph convolutional networks for semi-supervised classification" . | APPLIED INTELLIGENCE 55 . 6 (2025) . |
| APA | Chen, Zexi , Chen, Weibin , Yao, Jie , Li, Jinbo , Wang, Shiping . Deep random walk inspired multi-view graph convolutional networks for semi-supervised classification . | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
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Multi-view learning has demonstrated strong potential in processing data from different sources or viewpoints. Despite the significant progress made by Multi-view Graph Neural Networks (MvGNNs) in exploiting graph structures, features, and representations, existing research generally lacks architectures specifically designed for the intrinsic properties of multi-view data. This leads to models that still have deficiencies in fully utilizing consistent and complementary information in multi-view data. Most of current research tends to simply extend the single-view GNN framework to multi-view data, lacking in-depth strategies to handle and leverage the unique properties of these data. To address this issue, we propose a simple yet effective MvGNN framework called Multi-view Representation Learning with Decoupled private and shared Propagation (MvRL-DP). This framework enables multi-view data to be effectively processed as a whole by alternating private and shared operations to integrate cross-view information. In addition, to address possible inconsistencies between views, we present a discriminative loss that promotes class separability and prevents the model from being misled by noise hidden in multi-view data. Experiments demonstrate that the proposed framework is superior to current state-of-the-art methods in the multi-view semi-supervised classification task.
Keyword ：
Multi-view learning Multi-view learning Propagation decoupling Propagation decoupling Representation learning Representation learning Semi-supervised classification Semi-supervised classification Tensor operation Tensor operation
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| GB/T 7714 | Wang, Xuzheng , Lan, Shiyang , Wu, Zhihao et al. Multi-view Representation Learning with Decoupled private and shared Propagation [J]. | KNOWLEDGE-BASED SYSTEMS , 2025 , 310 . |
| MLA | Wang, Xuzheng et al. "Multi-view Representation Learning with Decoupled private and shared Propagation" . | KNOWLEDGE-BASED SYSTEMS 310 (2025) . |
| APA | Wang, Xuzheng , Lan, Shiyang , Wu, Zhihao , Guo, Wenzhong , Wang, Shiping . Multi-view Representation Learning with Decoupled private and shared Propagation . | KNOWLEDGE-BASED SYSTEMS , 2025 , 310 . |
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Recently, heterogeneous graphs have attracted widespread attention as a powerful and practical superclass of traditional homogeneous graphs, which reflect the multi-type node entities and edge relations in the real world. Most existing methods adopt meta-path construction as the mainstream to learn long-range heterogeneous semantic messages between nodes. However, such schema constructs the node-wise correlation by connecting nodes via pre-computed fixed paths, which neglects the diversities of meta-paths on the path type and path range. In this paper, we propose a meta-path-based semantic embedding schema, which is called Heterogeneous Graph Embedding with Dual Edge Differentiation (HGE-DED) to adequately construct flexible meta-path combinations thus learning the rich and discriminative semantic of target nodes. Concretely, HGEDED devises a Multi-Type and multi-Range Meta-Path Construction (MTR-MP Construction), which covers the comprehensive exploration of meta-path combinations from path type and path range, expressing the diversity of edges at more fine-grained scales. Moreover, HGE-DED designs the semantics and meta-path joint guidance, constructing a hierarchical short- and long-range relation adjustment, which constrains the path learning as well as minimizes the impact of edge heterophily on heterogeneous graphs. Experimental results on four benchmark datasets demonstrate the effectiveness of HGE-DED compared with state-of-the-art methods.
Keyword ：
Graph neural network Graph neural network Heterogeneous information network Heterogeneous information network Meta-path combination Meta-path combination Semantic embedding Semantic embedding Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Chen, Yuhong , Chen, Fuhai , Wu, Zhihao et al. Heterogeneous Graph Embedding with Dual Edge Differentiation [J]. | NEURAL NETWORKS , 2025 , 183 . |
| MLA | Chen, Yuhong et al. "Heterogeneous Graph Embedding with Dual Edge Differentiation" . | NEURAL NETWORKS 183 (2025) . |
| APA | Chen, Yuhong , Chen, Fuhai , Wu, Zhihao , Chen, Zhaoliang , Cai, Zhiling , Tan, Yanchao et al. Heterogeneous Graph Embedding with Dual Edge Differentiation . | NEURAL NETWORKS , 2025 , 183 . |
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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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Graph structure is widely used in the field of multi-view learning. Hypergraph which is a kind of extension of graph can capture the higher-order relationships of nodes in a better way. However, most existing hypergraph-based models are based on the assumption that hypergraph structures are readily available, which is untenable in most cases. In order to alleviate this problem, we propose the learnable unified hypergraph dynamic system framework, a novel approach in unified cross-view hypergraph structure generation tailored for multi-view semi-supervised classification. Specifically, we introduce four strategies for unified cross-view hypergraph generation and propose a mechanism for generating learnable unified cross-view hypergraph. Furthermore, we utilize a dynamic diffusion model to dynamically learn unified hypergraph structure which can achieve better performance in multi-view semi-supervised classification tasks. Extensive experimental results on various real datasets show that the proposed method outperforms other state-of-the-art multi-view algorithms. © 2025 Elsevier Ltd
Keyword ：
Hypergraph dynamic system Hypergraph dynamic system Multi-view hypergraph generation Multi-view hypergraph generation Multi-view learning Multi-view learning Semi-supervised classification Semi-supervised classification Unified cross-view hypergraph Unified cross-view hypergraph
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| GB/T 7714 | Shi, Z. , Lin, Z. , Lin, W. et al. Exploring unified cross-view hypergraph generation for multi-view semi-supervised classification [J]. | Neural Networks , 2025 , 188 . |
| MLA | Shi, Z. et al. "Exploring unified cross-view hypergraph generation for multi-view semi-supervised classification" . | Neural Networks 188 (2025) . |
| APA | Shi, Z. , Lin, Z. , Lin, W. , Wang, S. . Exploring unified cross-view hypergraph generation for multi-view semi-supervised classification . | Neural Networks , 2025 , 188 . |
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Many representation learning methods have gradually emerged to better exploit the properties of multi-view data. However, these existing methods still have the following areas to be improved: 1) Most of them overlook the ex-ante interpretability of the model, which renders the model more complex and more difficult for people to understand; 2) They underutilize the potential of the bi-topological spaces, which bring additional structural information to the representation learning process. This lack is detrimental when dealing with data that exhibits topological properties or has complex geometrical relationships between different views. Therefore, to address the above challenges, we propose an optimization-oriented multi-view representation learning framework in implicit bi-topological spaces. On one hand, we construct an intrinsically interpretability end-to-end white-box model that directly conducts the representation learning procedure while improving the transparency of the model. On the other hand, the integration of bi-topological spaces information within the network via manifold learning facilitates the comprehensive utilization of information from the data, ultimately enhancing representation learning and yielding superior performance for downstream tasks. Extensive experimental results demonstrate that the proposed method exhibits promising performance and is feasible in the downstream tasks.
Keyword ：
Bi-topological spaces Bi-topological spaces Multi-view learning Multi-view learning Optimization-oriented network Optimization-oriented network Representation learning Representation learning White-box model White-box model
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| GB/T 7714 | Lan, Shiyang , Du, Shide , Fang, Zihan et al. Optimization-oriented multi-view representation learning in implicit bi-topological spaces [J]. | INFORMATION SCIENCES , 2025 , 704 . |
| MLA | Lan, Shiyang et al. "Optimization-oriented multi-view representation learning in implicit bi-topological spaces" . | INFORMATION SCIENCES 704 (2025) . |
| APA | Lan, Shiyang , Du, Shide , Fang, Zihan , Cai, Zhiling , Huang, Wei , Wang, Shiping . Optimization-oriented multi-view representation learning in implicit bi-topological spaces . | INFORMATION SCIENCES , 2025 , 704 . |
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Graph convolutional networks have achieved remarkable success in the field of multi-view learning. Unfortunately, most graph convolutional network-based multi-view learning methods fail to capture long-range dependencies due to the over-smoothing problem. Many studies have attempted to mitigate this issue by decoupling graph convolution operations. However, these decoupled architectures lead to the absence of feature transformation module, thus limiting the expressive power of the model. To this end, we propose an information-controlled graph convolutional network for multi-view semi-supervised classification. In the proposed method, we maintain the paradigm of node embeddings during propagation by imposing orthogonality constraints on the feature transformation module. By further introducing a damping factor based on residual connections, we theoretically demonstrate that the proposed method can alleviate the over-smoothing problem while retaining the feature transformation module. Furthermore, we prove that the proposed model can stabilize both forward inference and backward propagation in graph convolutional networks. Extensive experimental results on benchmark datasets demonstrate the effectiveness of the proposed method.
Keyword ：
Graph convolutional network Graph convolutional network Layer normalization Layer normalization Multi-view learning Multi-view learning Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Shi, Yongquan , Pi, Yueyang , Liu, Zhanghui et al. Information-controlled graph convolutional network for multi-view semi-supervised classification [J]. | NEURAL NETWORKS , 2025 , 184 . |
| MLA | Shi, Yongquan et al. "Information-controlled graph convolutional network for multi-view semi-supervised classification" . | NEURAL NETWORKS 184 (2025) . |
| APA | Shi, Yongquan , Pi, Yueyang , Liu, Zhanghui , Zhao, Hong , Wang, Shiping . Information-controlled graph convolutional network for multi-view semi-supervised classification . | NEURAL NETWORKS , 2025 , 184 . |
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Multi-view learning has attracted considerable attention owing to its capability to learn more comprehensive representations. Although graph convolutional networks have achieved encouraging results in multi-view research, their limitation to considering only nearest neighbors results in the decrease on the ability to obtain high-order information. Many existing methods acquire high-order correlation by stacking multiple layers onto the model, yet they could lead to the issue of over-smoothing. In this paper, we propose a framework termed multi-scale graph diffusion convolutional network, which aims to gather comprehensive higher-order information without stacking multiple convolutional layers. Specifically, in order to better expand the receptive field of the node and reduce the parameter complexity, the proposed framework utilizes a contractive mapping to transform features from multiple views on decoupled propagation rules. Our framework introduces a multi-scale graph-based diffusion mechanism to adaptively extract the abundant high-order knowledge embedded within multi-scale graphs. Experiments show that the proposed method outperforms other state-of-the-art methods in terms of multi-view semi-supervised classification.
Keyword ：
Graph convolutional network Graph convolutional network Graph diffusion Graph diffusion Multi-scale fusion Multi-scale fusion Multi-view learning Multi-view learning Semi-supervised classification. Semi-supervised classification.
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| GB/T 7714 | Wang, Shiping , Li, Jiacheng , Chen, Yuhong et al. Multi-scale graph diffusion convolutional network for multi-view learning [J]. | ARTIFICIAL INTELLIGENCE REVIEW , 2025 , 58 (6) . |
| MLA | Wang, Shiping et al. "Multi-scale graph diffusion convolutional network for multi-view learning" . | ARTIFICIAL INTELLIGENCE REVIEW 58 . 6 (2025) . |
| APA | Wang, Shiping , Li, Jiacheng , Chen, Yuhong , Wu, Zhihao , Huang, Aiping , Zhang, Le . Multi-scale graph diffusion convolutional network for multi-view learning . | ARTIFICIAL INTELLIGENCE REVIEW , 2025 , 58 (6) . |
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Biomedical relation extraction aims to identify underlying relationships among entities, such as gene associations and drug interactions, within biomedical texts. Despite advancements in relation extraction in general knowledge domains, the scarcity of labeled training data remains a significant challenge in the biomedical field. This paper provides a novel approach for biomedical relation extraction that leverages a noisy student self-training strategy combined with negative learning. This method addresses the challenge of data insufficiency by utilizing distantly supervised data to generate high-quality labeled samples. Negative learning, as opposed to traditional positive learning, offers a more robust mechanism to discern and relabel noisy samples, preventing model overfitting. The integration of these techniques ensures enhanced noise reduction and relabeling capabilities, leading to improved performance even with noisy datasets. Experimental results demonstrate the effectiveness of the proposed framework in mitigating the impact of noisy data and outperforming existing benchmarks. IEEE
Keyword ：
Biological system modeling Biological system modeling Biomedical relation extraction Biomedical relation extraction Data mining Data mining Data models Data models distant supervision distant supervision negative learning negative learning Noise measurement Noise measurement noisy student self-training noisy student self-training Stomach Stomach Training Training Training data Training data
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| GB/T 7714 | Dai, Y. , Zhang, B. , Wang, S. . Distantly Supervised Biomedical Relation Extraction Via Negative Learning and Noisy Student Self-Training [J]. | ACM Transactions on Computational Biology and Bioinformatics , 2024 : 1-12 . |
| MLA | Dai, Y. et al. "Distantly Supervised Biomedical Relation Extraction Via Negative Learning and Noisy Student Self-Training" . | ACM Transactions on Computational Biology and Bioinformatics (2024) : 1-12 . |
| APA | Dai, Y. , Zhang, B. , Wang, S. . Distantly Supervised Biomedical Relation Extraction Via Negative Learning and Noisy Student Self-Training . | ACM Transactions on Computational Biology and Bioinformatics , 2024 , 1-12 . |
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