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学者姓名：孙艳丰
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Abstract ：
It has been proven that Graph Neural Networks focus more on the majority class instances and ignore minority class instances when the class distribution is imbalanced. To address the class imbalance problems on graphs, most of the existing approaches rely on the availability of minority nodes in the training set, which may be scarce in extremely imbalanced situations and lead to overfitting. To tackle this issue, this paper proposes a novel oversampling-based Neighbor imbalanced-aware Graph Neural Networks, abbreviated as Nia-GNNs. Specifically, we propose a novel interpolation method that selects interpolated minority nodes from the entire dataset according to their predicted labels and similarity. Meanwhile, a class-wise interpolation ratio is applied to prevent the generation of out-of-domain nodes. Additionally, the generated minority nodes are inserted into the neighbor of minority nodes according to their neighbor distribution to balance the graph both neighborly and globally. Numerous experiments on different imbalanced datasets demonstrate the superiority of our method in classifying imbalanced nodes.
Keyword ：
Class imbalance learning Class imbalance learning Node classification Node classification Oversampling Oversampling Graph neural networks Graph neural networks
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| GB/T 7714 | Sun, Yanfeng , Wang, Yujia , Wang, Shaofan . Nia-GNNs: neighbor-imbalanced aware graph neural networks for imbalanced node classification [J]. | APPLIED INTELLIGENCE , 2024 , 54 (17-18) : 7941-7957 . |
| MLA | Sun, Yanfeng 等. "Nia-GNNs: neighbor-imbalanced aware graph neural networks for imbalanced node classification" . | APPLIED INTELLIGENCE 54 . 17-18 (2024) : 7941-7957 . |
| APA | Sun, Yanfeng , Wang, Yujia , Wang, Shaofan . Nia-GNNs: neighbor-imbalanced aware graph neural networks for imbalanced node classification . | APPLIED INTELLIGENCE , 2024 , 54 (17-18) , 7941-7957 . |
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Abstract ：
Superpixel segmentation, which amounts to partitioning an image into a number of superpixels each of which is a set of pixels sharing common visual meanings, requires specific needs for different computer vision tasks. Graph based methods, as a kind of popular superpixel segmentation method, regard an image as a weighted graph whose nodes correspond to pixels of the image, and partition all pixels into superpixels according to the similarity between pixels over various feature spaces. Despite their improvement of the performance of segmentation, these methods ignore high-order relationship between them incurred from either locally neighboring pixels or structured layout of the image. Moreover, they measure the similarity of pairwise pixels using Gaussian kernel where a robust radius parameter is difficult to find for pixels which exhibit multiple features (e.g., texture, color, brightness). In this paper, we propose an adaptive hypergraph superpixel segmentation (AHS) of intensity images for solving both issues. AHS constructs a hypergraph by building the hyperedges with an adaptive neighborhood scheme, which explores an intrinsic relationship of pixels. Afterwards, AHS encodes the relationship between pairwise pixels using characteristics of current two pixels as well as their neighboring pixels defined by hyperedges. Essentially, AHS models the relationship of pairwise pixels in a high-order group fashion while graph based methods evaluate it in a one-vs-one fashion. Experiments on four datasets demonstrate that AHS achieves higher or comparable performance compared with state-of-the-art methods.
Keyword ：
Superpixel segmentation Superpixel segmentation Hypergraph cut Hypergraph cut
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| GB/T 7714 | Wang, Shaofan , Lan, Jiaqi , Lin, Jing et al. Adaptive hypergraph superpixels [J]. | DISPLAYS , 2023 , 76 . |
| MLA | Wang, Shaofan et al. "Adaptive hypergraph superpixels" . | DISPLAYS 76 (2023) . |
| APA | Wang, Shaofan , Lan, Jiaqi , Lin, Jing , Liu, Yukun , Wang, Lichun , Sun, Yanfeng et al. Adaptive hypergraph superpixels . | DISPLAYS , 2023 , 76 . |
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Abstract ：
Graph convolutional networks (GCNs) have become a popular tool for learning unstructured graph data due to their powerful learning ability. Many researchers have been interested in fusing topological structures and node features to extract the correlation information for classification tasks. However, it is inadequate to integrate the embedding from topology and feature spaces to gain the most correlated information. At the same time, most GCN-based methods assume that the topology graph or feature graph is compatible with the properties of GCNs, but this is usually not satisfied since meaningless, missing, or even unreal edges are very common in actual graphs. To obtain a more robust and accurate graph structure, we intend to construct an adaptive graph with topology and feature graphs. We propose Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision (MFGCN), which learn a connected embedding by fusing the multi-graphs and node features. We can obtain the final node embedding for semi-supervised node classification by propagating node features over multi-graphs. Furthermore, to alleviate the problem of labels missing in semi-supervised classification, a pseudo-label generation mechanism is proposed to generate more reliable pseudo-labels based on the similarity of node features. Extensive experiments on six benchmark datasets demonstrate the superiority of MFGCN over state-of-the-art classification methods.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword ：
Pseudo -label supervision Pseudo -label supervision Graph convolutional networks Graph convolutional networks Semi -supervised learning Semi -supervised learning Node classification Node classification
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| GB/T 7714 | Yang, Yachao , Sun, Yanfeng , Ju, Fujiao et al. Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision [J]. | NEURAL NETWORKS , 2023 , 158 : 305-317 . |
| MLA | Yang, Yachao et al. "Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision" . | NEURAL NETWORKS 158 (2023) : 305-317 . |
| APA | Yang, Yachao , Sun, Yanfeng , Ju, Fujiao , Wang, Shaofan , Gao, Junbin , Yin, Baocai . Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision . | NEURAL NETWORKS , 2023 , 158 , 305-317 . |
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Abstract ：
Structured clustering networks, which alleviate the oversmoothing issue by delivering hidden features from autoencoder (AE) to graph convolutional networks (GCNs), involve two shortcomings for the clustering task. For one thing, they used vanilla structure to learn clustering representations without considering feature and structure corruption; for another thing, they exhibit network degradation and vanishing gradient issues after stacking multilayer GCNs. In this article, we propose a clustering method called dual-masked deep structural clustering network (DMDSC) with adaptive bidirectional information delivery (ABID). Specifically, DMDSC enables generative self-supervised learning to mine deeper interstructure and interfeature correlations by simultaneously reconstructing corrupted structures and features. Furthermore, DMDSC develops an ABID module to establish an information transfer channel between each pairwise layer of AE and GCNs to alleviate the oversmoothing and vanishing gradient problems. Numerous experiments on six benchmark datasets have shown that the proposed DMDSC outperforms the most advanced deep clustering algorithms.
Keyword ：
Index Terms-Deep clustering Index Terms-Deep clustering network representation learning network representation learning graph convolutional net-works (GCNs) graph convolutional net-works (GCNs) self-supervised learning self-supervised learning
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| GB/T 7714 | Yang, Yachao , Sun, Yanfeng , Wang, Shaofan et al. A Dual-Masked Deep Structural Clustering Network With Adaptive Bidirectional Information Delivery [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2023 . |
| MLA | Yang, Yachao et al. "A Dual-Masked Deep Structural Clustering Network With Adaptive Bidirectional Information Delivery" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023) . |
| APA | Yang, Yachao , Sun, Yanfeng , Wang, Shaofan , Gao, Junbin , Ju, Fujiao , Yin, Baocai . A Dual-Masked Deep Structural Clustering Network With Adaptive Bidirectional Information Delivery . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2023 . |
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Abstract ：
Referring expression comprehension aims to locate a target object in an image described by a referring expression, where extracting semantic and discriminative visual information plays an important role. Most existing methods either ignore attribute information or context information in the model learning procedure, thus resulting in less effective visual features. In this paper, we propose a Multi-level Attention Network (MANet) to extract the target attribute information and the surrounding context information simultaneously for the target object, where the Attribute Attention Module is designed to extract the fine-grained visual information related to the referring expression and the Context Attention Module is designed to merge the context information of surroundings to learn more discriminative visual features. Experiments on various common benchmark datasets show the effectiveness of our approach.& COPY; 2023 Elsevier B.V. All rights reserved.
Keyword ：
Context information Context information Attribute information Attribute information Multilevel attention Multilevel attention
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| GB/T 7714 | Sun, Yanfeng , Zhang, Yunru , Jiang, Huajie et al. Multi-level attention for referring expression comprehension [J]. | PATTERN RECOGNITION LETTERS , 2023 , 172 : 252-258 . |
| MLA | Sun, Yanfeng et al. "Multi-level attention for referring expression comprehension" . | PATTERN RECOGNITION LETTERS 172 (2023) : 252-258 . |
| APA | Sun, Yanfeng , Zhang, Yunru , Jiang, Huajie , Hu, Yongli , Yin, Baocai . Multi-level attention for referring expression comprehension . | PATTERN RECOGNITION LETTERS , 2023 , 172 , 252-258 . |
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Abstract ：
The effective modal fusion and perception between the language and the image are necessary for inferring the reference instance in the referring image segmentation (RIS) task. In this article, we propose a novel RIS network, the global and local interactive perception network (GLIPN), to enhance the quality of modal fusion between the language and the image from the local and global perspectives. The core of GLIPN is the global and local interactive perception (GLIP) scheme. Specifically, the GLIP scheme contains the local perception module (LPM) and the global perception module (GPM). The LPM is designed to enhance the local modal fusion by the correspondence between word and image local semantics. The GPM is designed to inject the global structured semantics of images into the modal fusion process, which can better guide the word embedding to perceive the whole image's global structure. Combined with the local-global context semantics fusion, extensive experiments on several benchmark datasets demonstrate the advantage of the proposed GLIPN over most state-of-the-art approaches.
Keyword ：
referring image segmentation (RIS) referring image segmentation (RIS) Visualization Visualization transformer transformer Feature extraction Feature extraction Object detection Object detection Attention mechanism Attention mechanism Semantics Semantics global perception global perception local perception local perception Image segmentation Image segmentation Detectors Detectors Task analysis Task analysis
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| GB/T 7714 | Liu, Jing , Tan, Hongchen , Hu, Yongli et al. Global and Local Interactive Perception Network for Referring Image Segmentation [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2023 . |
| MLA | Liu, Jing et al. "Global and Local Interactive Perception Network for Referring Image Segmentation" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023) . |
| APA | Liu, Jing , Tan, Hongchen , Hu, Yongli , Sun, Yanfeng , Wang, Huasheng , Yin, Baocai . Global and Local Interactive Perception Network for Referring Image Segmentation . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2023 . |
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Abstract ：
本发明涉及一种基于对偶动态时空图卷积的交通预测方法，用于解决当前基于图网络的交通预测方法中存在缺少对边建模以及动态建模导致的预测精度不高的问题。首先输入历史交通数据，送到输入层进行处理，然后将输入层的输出送入动态时空层，经过动态时空层中多个堆叠的对偶动态时空块进行时空相关性特征抽取，再将这些特征输入到输出层，最后输出的即是最终的预测结果。其中，最核心和关键的对偶动态时空块包括动态图卷积模块、动态超图卷积模块，以及两个之间的动态交互模块。本发明能很好的挖掘交通数据中复杂的时空相关性，从而揭示动态交通系统潜在的时空关联，进而更加准确的对城市交通数据进行预测。
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| GB/T 7714 | 孙艳丰 , 江相衡 , 胡永利 et al. 一种基于对偶动态时空图卷积的交通预测方法 : CN202210096933.X[P]. | 2022-01-26 . |
| MLA | 孙艳丰 et al. "一种基于对偶动态时空图卷积的交通预测方法" : CN202210096933.X. | 2022-01-26 . |
| APA | 孙艳丰 , 江相衡 , 胡永利 , 郭侃 , 尹宝才 . 一种基于对偶动态时空图卷积的交通预测方法 : CN202210096933.X. | 2022-01-26 . |
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Abstract ：
Graph-based clustering learns underlying data representation by employing topological graph structure. Recently, Graph Convolutional Network (GCN)-based clustering methods have accumulated great attentions and achieved great performance. Its performance is seriously determined by the quality of pre-provided graph, which is usually constructed by predefined model (such as k-Nearest-Neighbor). However, the graph may be inaccurate due to the noises and fixed graph limits flexibility of model learning. In this paper, we propose a Robust Graph Convolutional Clustering (RGCC) method, which adaptively learns a clean and accurate graph from original graph. Specifically, adaptive graph with low-rank and sparse structures be learned during the optimization process, which can better encode structural information of data than fixed graph. Then, to explore the local connectivity of data, graph Laplacian constraint is introduced. Thus, optimal graph relationships and discriminative representation of data could be simultaneously learned, which improves the flexibility of the RGCC model. By designing a self-supervised clustering module, it can self-supervise the node representations learning and thus explore the better clustering structure. Experimental results on several benchmark databases reveal the superiority of the proposed RGCC approach.
Keyword ：
Graph convolution network Graph convolution network Low-rank and sparse graph Low-rank and sparse graph Adaptive graph learning Adaptive graph learning Self-supervised learning Self-supervised learning Attributed graph clustering Attributed graph clustering
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| GB/T 7714 | Zhao, Jiayi , Sun, Yanfeng , Guo, Jipeng et al. Robust Graph Convolutional Clustering With Adaptive Graph Learning [J]. | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) , 2022 . |
| MLA | Zhao, Jiayi et al. "Robust Graph Convolutional Clustering With Adaptive Graph Learning" . | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (2022) . |
| APA | Zhao, Jiayi , Sun, Yanfeng , Guo, Jipeng , Gao, Junbin , Yin, Baocai . Robust Graph Convolutional Clustering With Adaptive Graph Learning . | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) , 2022 . |
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Abstract ：
Dimensionality reduction plays an important role in image recognition and data mining Traditional methods extract features from data itself and ignore the structure information of data even though it is crucial for effective representation. Considering graph embedding method can capture and model the complicated relationships among data, therefore, we consider to incorporate graph convolution learning into principal component analysis (GCPCA) to abstract more effective features in this paper. The key idea of the proposed model is embedding graph convolutional to realize linear representation by fusing the relationship of data points. Then PCA is operated on projected data to extract effective features. The model can be solved to obtain a globally optimal closed-form solution, which is convenient for implementation and practical application. Experiments on some publicly available datasets demonstrate that the proposed GCPCA model show the better performance than the existing classical algorithms in terms of classification accuracy.
Keyword ：
Principal component analysis Principal component analysis Dimensionality reduction Dimensionality reduction Feature extraction Feature extraction Graph embedding Graph embedding
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| GB/T 7714 | Ju, Fujiao , Sun, Yanfeng , Li, Jianqiang et al. Principal component analysis based on graph embedding [J]. | MULTIMEDIA TOOLS AND APPLICATIONS , 2022 , 82 (5) : 7105-7116 . |
| MLA | Ju, Fujiao et al. "Principal component analysis based on graph embedding" . | MULTIMEDIA TOOLS AND APPLICATIONS 82 . 5 (2022) : 7105-7116 . |
| APA | Ju, Fujiao , Sun, Yanfeng , Li, Jianqiang , Zhang, Yaxiao , Piao, Xinglin . Principal component analysis based on graph embedding . | MULTIMEDIA TOOLS AND APPLICATIONS , 2022 , 82 (5) , 7105-7116 . |
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Abstract ：
The graph convolutional network (GCN)-based clustering approaches have achieved the impressive performance due to strong ability of exploiting the topological structure. The adjacency graph seriously affects the clustering performance, especially for non-graph data. Existing approaches usually conduct two independent steps, i.e., constructing a fixed graph structure and then graph embedding representation learning by GCN. However, the constructed graph structure may be unreliable one due to noisy data, resulting in sub-optimal graph embedding representation. In this paper, we propose an adaptive graph convolutional clustering network (AGCCN) to alternatively learn the similarity graph structure and node embedding representation in a unified framework. Our AGCCN learns the weighted adjacency graph adaptively from the node representations by solving the optimization problem of graph learning, in which adaptive and optimal neighbors for each sample are assigned with probabilistic way according to local connectivity. Then, the attribute feature extracted by parallel Auto-Encoder (AE) module is fused into the input of adaptive graph convolution module layer-by-layer to learn the comprehensive node embedding representation and strengthen its representation ability. This also skillfully alleviates the over-smoothing problem of GCN. To further improve the discriminant ability of node representation, a dual self-supervised clustering mechanism is designed to guide model optimization with pseudo-labels information. Extensive experimental results on various real-world datasets consistently show the superiority and effectiveness of the proposed deep graph clustering method.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword ：
Adaptive graph structure learning Adaptive graph structure learning Graph convolutional network Graph convolutional network Self-supervised learning Self-supervised learning Deep clustering Deep clustering
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| GB/T 7714 | Zhao, Jiayi , Guo, Jipeng , Sun, Yanfeng et al. Adaptive graph convolutional clustering network with optimal probabilistic graph [J]. | NEURAL NETWORKS , 2022 , 156 : 271-284 . |
| MLA | Zhao, Jiayi et al. "Adaptive graph convolutional clustering network with optimal probabilistic graph" . | NEURAL NETWORKS 156 (2022) : 271-284 . |
| APA | Zhao, Jiayi , Guo, Jipeng , Sun, Yanfeng , Gao, Junbin , Wang, Shaofan , Yin, Baocai . Adaptive graph convolutional clustering network with optimal probabilistic graph . | NEURAL NETWORKS , 2022 , 156 , 271-284 . |
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