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学者姓名：王石平
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Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in the real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this article, we apply Graph Convolutional Network (GCN) to cope with heterogeneous graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top-k nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module, and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks, and the experimental results indicate that MGCN-DNS achieves pleasurable performance on several benchmark multi-view datasets.
Keyword ：
differentiable node selection differentiable node selection graph convolutional network graph convolutional network Multi-view learning Multi-view learning semi-supervised classification semi-supervised classification
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| GB/T 7714 | Chen, Zhaoliang , Fu, Lele , Xiao, Shunxin et al. Multi-View Graph Convolutional Networks with Differentiable Node Selection [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2024 , 18 (1) . |
| MLA | Chen, Zhaoliang et al. "Multi-View Graph Convolutional Networks with Differentiable Node Selection" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 18 . 1 (2024) . |
| APA | Chen, Zhaoliang , Fu, Lele , Xiao, Shunxin , Wang, Shiping , Plant, Claudia , Guo, Wenzhong . Multi-View Graph Convolutional Networks with Differentiable Node Selection . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2024 , 18 (1) . |
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Deep learning-based clustering methods, especially those incorporating deep generative models, have recently shown noticeable improvement on many multimedia benchmark datasets. However, existing generative models still suffer from unstable training, and the gradient vanishes, which results in the inability to learn desirable embedded features for clustering. In this paper, we aim to tackle this problem by exploring the capability of Wasserstein embedding in learning representative embedded features and introducing a new clustering module for jointly optimizing embedding learning and clustering. To this end, we propose Wasserstein embedding clustering (WEC), which integrates robust generative models with clustering. By directly minimizing the discrepancy between the prior and marginal distribution, we transform the optimization problem of Wasserstein distance from the original data space into embedding space, which differs from other generative approaches that optimize in the original data space. Consequently, it naturally allows us to construct a joint optimization framework with the designed clustering module in the embedding layer. Due to the substitutability of the penalty term in Wasserstein embedding, we further propose two types of deep clustering models by selecting different penalty terms. Comparative experiments conducted on nine publicly available multimedia datasets with several state-of-the-art methods demonstrate the effectiveness of our method.
Keyword ：
auto-encoder auto-encoder clustering analysis clustering analysis Clustering methods Clustering methods Data models Data models Decoding Decoding Deep learning Deep learning Generative adversarial networks Generative adversarial networks generative models generative models Task analysis Task analysis Training Training Unsupervised learning Unsupervised learning Wasserstein embedding Wasserstein embedding
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| GB/T 7714 | Cai, Jinyu , Zhang, Yunhe , Wang, Shiping et al. Wasserstein Embedding Learning for Deep Clustering: A Generative Approach [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 7567-7580 . |
| MLA | Cai, Jinyu et al. "Wasserstein Embedding Learning for Deep Clustering: A Generative Approach" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 7567-7580 . |
| APA | Cai, Jinyu , Zhang, Yunhe , Wang, Shiping , Fan, Jicong , Guo, Wenzhong . Wasserstein Embedding Learning for Deep Clustering: A Generative Approach . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 7567-7580 . |
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Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi -relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta -paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi -Order Graph Convolutional Network (AMOGCN), which automatically explores meta -paths that involve multi -hop neighbors by aggregating multi -order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi -order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one -layer simplifying graph convolutional network with the learned multi -order adjacency matrix, which is equivalent to the cross -hop node information propagation with multilayer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi -supervised classification performance compared with state-of-the-art competitors.
Keyword ：
Graph convolutional networks Graph convolutional networks Heterogeneous graphs Heterogeneous graphs Multi-order adjacency matrix Multi-order adjacency matrix Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Chen, Zhaoliang , Wu, Zhihao , Zhong, Luying et al. Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs [J]. | NEURAL NETWORKS , 2024 , 174 . |
| MLA | Chen, Zhaoliang et al. "Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs" . | NEURAL NETWORKS 174 (2024) . |
| APA | Chen, Zhaoliang , Wu, Zhihao , Zhong, Luying , Plant, Claudia , Wang, Shiping , Guo, Wenzhong . Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs . | NEURAL NETWORKS , 2024 , 174 . |
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With the rapid growth of activities on the web, large amounts of interaction data on multimedia platforms are easily accessible, including e-commerce, music sharing, and social media. By discovering various interests of users, recommender systems can improve user satisfaction without accessing overwhelming personal information. Compared to graph-based models, hypergraph-based collaborative filtering has the ability to model higher-order relations besides pair-wise relations among users and items, where the hypergraph structures are mainly obtained from specialized data or external knowledge. However, the above well-constructed hypergraph structures are often not readily available in every situation. To this end, we first propose a novel framework named HGRec, which can enhance recommendation via automatic hypergraph generation. By exploiting the clustering mechanism based on the user/item similarity, we group users and items without additional knowledge for hypergraph structure learning and design a cross-view recommendation module to alleviate the combinatorial gaps between the representations of the local ordinary graph and the global hypergraph. Furthermore, we devise a sparse optimization strategy to ensure the effectiveness of hypergraph structures, where a novel integration of the l( 2,1)-norm and optimal transport framework is designed for hypergraph generation. We term the model HGRec with sparse optimization strategy as HGRec++. Extensive experiments on public multi-domain datasets demonstrate the superiority brought by our HGRec++, which gains average 8.1% and 9.8% improvement over state-of-the-art baselines regarding Recall and NDCG metrics, respectively.
Keyword ：
graph convolutional network graph convolutional network hypergraph generation hypergraph generation Recommender systems Recommender systems sparse optimization sparse optimization
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| GB/T 7714 | Lin, Zhenghong , Yan, Qishan , Liu, Weiming et al. Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 5680-5693 . |
| MLA | Lin, Zhenghong et al. "Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 5680-5693 . |
| APA | Lin, Zhenghong , Yan, Qishan , Liu, Weiming , Wang, Shiping , Wang, Menghan , Tan, Yanchao et al. Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 5680-5693 . |
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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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Graph neural networks entail massive labeled samples for training, and manual labeling generally requires unaffordable costs. Active learning has emerged as a promising approach to selecting a smaller set of informative labeled samples to improve model performance. However, few active learning techniques for graph data account for the cluster structure and redundancy of samples. To address these issues, we propose an approach that employs uncertain information as an observation for a reinforcement learning agent to adaptively learn a node selection policy. We construct states using node information obtained via mutual information, which considers both the graph structure and the node attributes. The proposed method accurately quantifies node information by leveraging the receptive field of the graph convolutional network and capturing the clustering structure of the data, taking into account the low redundancy and diversity of the labeled samples. Experiments conducted on real-world datasets demonstrate the superiority of the proposed approach over several state-of-the-art methods.
Keyword ：
Active learning Active learning Deep learning Deep learning Graph convolutional network Graph convolutional network Mutual information Mutual information Reinforcement learning Reinforcement learning
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| GB/T 7714 | Huang, Yang , Pi, Yueyang , Shi, Yiqing et al. Adaptive graph active learning with mutual information via policy learning [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
| MLA | Huang, Yang et al. "Adaptive graph active learning with mutual information via policy learning" . | EXPERT SYSTEMS WITH APPLICATIONS 255 (2024) . |
| APA | Huang, Yang , Pi, Yueyang , Shi, Yiqing , Guo, Wenzhong , Wang, Shiping . Adaptive graph active learning with mutual information via policy learning . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
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BackgroundAchieving precise cancer subtype classification is imperative for effective prognosis and treatment. Multi-omics studies, encompassing diverse data modalities, have emerged as powerful tools for unraveling the complexities of cancer. However, owing to the intricacies of biological data, multi-omics datasets generally show variations in data types, scales, and distributions. These intractable problems lead to challenges in exploring intact representations from heterogeneous data, which often result in inaccuracies in multi-omics information analysis.ResultsTo address the challenges of multi-omics research, our approach DeepMoIC presents a novel framework derived from deep Graph Convolutional Network (GCN). Leveraging autoencoder modules, DeepMoIC extracts compact representations from omics data and incorporates a patient similarity network through the similarity network fusion algorithm. To handle non-Euclidean data and explore high-order omics information effectively, we design a Deep GCN module with two strategies: residual connection and identity mapping. With extracted higher-order representations, our approach consistently outperforms state-of-the-art models on a pan-cancer dataset and 3 cancer subtype datasets.ConclusionThe introduction of Deep GCN shows encouraging performance in terms of supervised multi-omics feature learning, offering promising insights for precision medicine in cancer research. DeepMoIC can potentially be an important tool in the field of cancer subtype classification because of its capacity to handle complex multi-omics data and produce reliable classification findings.
Keyword ：
Cancer subtype classification Cancer subtype classification Deep graph convolutional network Deep graph convolutional network Multi-omics Multi-omics Supervised learning Supervised learning
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| GB/T 7714 | Wu, Jiecheng , Chen, Zhaoliang , Xiao, Shunxin et al. DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification [J]. | BMC GENOMICS , 2024 , 25 (1) . |
| MLA | Wu, Jiecheng et al. "DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification" . | BMC GENOMICS 25 . 1 (2024) . |
| APA | Wu, Jiecheng , Chen, Zhaoliang , Xiao, Shunxin , Liu, Genggeng , Wu, Wenjie , Wang, Shiping . DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification . | BMC GENOMICS , 2024 , 25 (1) . |
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Facing the increasing heterogeneity of data in the real world, multi-view learning has become a crucial area of research. Graph Convolutional Networks (GCNs) are powerful for modeling both graph structures and features, making them a focal point in multi-view learning research. However, these methods typically only account for static data dependencies within each view separately when constructing the topology necessary for GCNs, overlooking potential relationships across views in multi-view data. Furthermore, there is a notable absence of theoretical guidance for constructing multi-view data topologies, leading to uncertainty regarding the progression of graph embeddings toward a consistent state. To tackle these challenges, we introduce a framework named energy-constrained multi-view graph diffusion. This approach establishes a mathematical correspondence between multi-view data and GCNs via graph diffusion. It treats multi-view data as a unified entity and devises a feature propagation process with inter-view awareness by considering both inter-view and intra-view feature flow across the entire system. Additionally, an energy function is introduced to guide the inter- and intra-view diffusion, ensuring that the representations converge towards global consistency. The empirical research on several benchmark datasets substantiates the benefits of the proposed method. © 2024 ACM.
Keyword ：
Convolution Convolution Data flow graphs Data flow graphs Graph embeddings Graph embeddings Network theory (graphs) Network theory (graphs) Spatio-temporal data Spatio-temporal data
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| GB/T 7714 | Lu, Jielong , Wu, Zhihao , Chen, Zhaoliang et al. Towards Multi-view Consistent Graph Diffusion [C] . 2024 : 186-195 . |
| MLA | Lu, Jielong et al. "Towards Multi-view Consistent Graph Diffusion" . (2024) : 186-195 . |
| APA | Lu, Jielong , Wu, Zhihao , Chen, Zhaoliang , Cai, Zhiling , Wang, Shiping . Towards Multi-view Consistent Graph Diffusion . (2024) : 186-195 . |
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The inherent variability and unpredictability in open multi-view learning scenarios infuse considerable ambiguity into the learning and decision-making processes of predictors. This demands that predictors not only recognize familiar patterns but also adaptively interpret unknown ones out of training scope. To address this challenge, we propose an Ambiguity-Aware Multi-view Learning Framework, which integrates four synergistic modules into an end-to-end framework to achieve generalizability and reliability beyond the known. By introducing the mixed samples to broaden the learning sample space, accompanied by corresponding soft labels to encapsulate their inherent uncertainty, the proposed method adapts to the distribution of potentially unknown samples in advance. Furthermore, an instance-level sparse inference is implemented to learn sparse approximated points in the multiple view embedding space, and individual view representations are gated by view-level confidence mappings. Finally, a multi-view consistent representation is obtained by dynamically assigning weights based on the degree of cluster-level dispersion. Extensive experiments demonstrate that our approach is effective and stable compared with other state-of-the-art methods in open-world recognition situations. © 2024 ACM.
Keyword ：
Adversarial machine learning Adversarial machine learning Contrastive Learning Contrastive Learning Federated learning Federated learning Self-supervised learning Self-supervised learning Unsupervised learning Unsupervised learning
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| GB/T 7714 | Fang, Zihan , Du, Shide , Chen, Yuhong et al. Beyond the Known: Ambiguity-Aware Multi-view Learning [C] . 2024 : 8518-8526 . |
| MLA | Fang, Zihan et al. "Beyond the Known: Ambiguity-Aware Multi-view Learning" . (2024) : 8518-8526 . |
| APA | Fang, Zihan , Du, Shide , Chen, Yuhong , Wang, Shiping . Beyond the Known: Ambiguity-Aware Multi-view Learning . (2024) : 8518-8526 . |
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With the explosive growth of user-generated content, multi-view learning has become a rapidly growing direction in pattern recognition and data analysis areas. Due to the significant application value of multi-view learning, there has been a continuous emergence of research based on machine learning methods and traditional deep learning paradigms. The core challenge in multi-view learning lies in harnessing both consistent and complementary information to forge a unified, comprehensive representation. However, many multi-view learning tasks are based on graph-structured data, making existing methods unable to effectively mine the information contained in the input multiple data sources. Recently, graph neural networks (GNN) techniques have been widely utilized to deal with non-Euclidean data, such as graphs or manifolds. Thus, it is essential to combine the advantages of the powerful learning capability of GNN models and multi-view data. In this paper, we aim to provide a comprehensive survey of recent research works on GNN-based multi-view learning. In detail, we first provide a taxonomy of GNN-based multi-view learning methods according to the input form of models: multi-relation, multi-attribute and mixed. Then, we introduce the applications of multi-view learning, including recommendation systems, computer vision and so on. Moreover, several public datasets and open-source codes are introduced for implementation. Finally, we analyze the challenges of applying GNN models on various multi-view learning tasks and state new future directions in this field.
Keyword ：
Deep learning Deep learning Graph neural network Graph neural network Multi-view learning Multi-view learning Survey Survey
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| GB/T 7714 | Xiao, Shunxin , Li, Jiacheng , Lu, Jielong et al. Graph neural networks for multi-view learning: a taxonomic review [J]. | ARTIFICIAL INTELLIGENCE REVIEW , 2024 , 57 (12) . |
| MLA | Xiao, Shunxin et al. "Graph neural networks for multi-view learning: a taxonomic review" . | ARTIFICIAL INTELLIGENCE REVIEW 57 . 12 (2024) . |
| APA | Xiao, Shunxin , Li, Jiacheng , Lu, Jielong , Huang, Sujia , Zeng, Bao , Wang, Shiping . Graph neural networks for multi-view learning: a taxonomic review . | ARTIFICIAL INTELLIGENCE REVIEW , 2024 , 57 (12) . |
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