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Guoqiu Wen

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7 papers

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IJCAI Conference 2025 Conference Paper

Graph Embedded Contrastive Learning for Multi-View Clustering

Hongqing He
Jie Xu
Guoqiu Wen
Yazhou Ren
Na Zhao
Xiaofeng Zhu

Recently, numerous multi-view clustering (MVC) and multi-view graph clustering (MVGC) methods have been proposed. Despite significant progress, they still face two issues: I) MVC and MVGC are often developed independently for multi-view and multi-graph data. They have redundancy but lack a unified methodology to combine their strengths. II) Contrastive learning is usually adopted to explore the associations across multiple views. However, traditional contrastive losses ignore the neighbor relationship in multi-view scenarios and easily lead to false associations in sample pairs. To address these issues, we propose Graph Embedded Contrastive Learning for Multi-View Clustering. Concretely, we propose a process of view-specific pre-training with adaptive graph convolution to make our method compatible with both multi-view and multi-graph data, which aggregates the graph information into data and leverages autoencoders to learn view-specific representations. Furthermore, to explore the view-cross associations, we introduce the process of view-cross contrastive learning and clustering, where we propose the graph-guided contrastive learning that can generate global graph to mitigate the false association issue as well as the cluster-guided contrastive clustering for improving the model robustness. Finally, extensive experiments demonstrate that our method achieves superior performance on both MVC and MVGC tasks.

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AAAI Conference 2025 Conference Paper

Multiplex Graph Representation Learning with Homophily and Consistency

Yudi Huang
Ci Nie
Hongqing He
Yujie Mo
Yonghua Zhu
Guoqiu Wen
Xiaofeng Zhu

Although unsupervised multiplex graph representation learning (UMGRL) has been a hot research topic, existing UMGRL methods still has limitations to be addressed. For example, previous works either preserve structural information by ignoring the impact of heterophily in the graph structure or only focus on node-level consistency by ignoring class-level consistency. To address these issues, in this paper, we propose a new UMGRL method to explore both homophily and consistency in the multiplex graph. Specifically, we propose to restructure the multi-order relationships of every graph between every node and its multi-order neighbors to improve the homophily and reduce the impact of the heterophily in the graph structure. We also design a contrastive loss based on a self-expression matrix of the node representation to achieve node-level and class-level consistency. Furthermore, we theoretically prove our method to achieve class-level consistency. Extensive experimental results on real datasets verify the effectiveness of the proposed method with respect to node classification tasks, compared to SOTA methods.

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AAAI Conference 2025 Conference Paper

Noisy Node Classification by Bi-level Optimization Based Multi-Teacher Distillation

Yujing Liu
Zongqian Wu
Zhengyu Lu
Ci Nie
Guoqiu Wen
Yonghua Zhu
Xiaofeng Zhu

Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on the graph data. Specifically, we first employ multiple self-supervised learning methods to train diverse teacher models, and then aggregate their predictions through a teacher weight matrix. Furthermore, we design a new bi-level optimization strategy to dynamically adjust the teacher weight matrix based on the training progress of the student model. Finally, we design a label improvement module to improve the label quality. Extensive experimental results on real datasets show that our method achieves the best results compared to state-of-the-art methods.

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IJCAI Conference 2024 Conference Paper

Multiplex Graph Representation Learning via Bi-level Optimization

Yudi Huang
Yujie Mo
Yujing Liu
Ci Nie
Guoqiu Wen
Xiaofeng Zhu

Many multiplex graph representation learning (MGRL) methods have been demonstrated to 1) ignore the globally positive and negative relationships among node features; and 2) usually utilize the node classification task to train both graph structure learning and representation learning parameters, and thus resulting in the problem of edge starvation. To address these issues, in this paper, we propose a new MGRL method based on the bi-level optimization. Specifically, in the inner level, we optimize the self-expression matrix to capture the globally positive and negative relationships among nodes, as well as complement them with the local relationships in graph structures. In the outer level, we optimize the parameters of the graph convolutional layer to obtain discriminative node representations. As a result, the graph structure optimization does not depend on the node classification task, which solves the edge starvation problem. Extensive experiments show that our model achieves the superior performance on node classification tasks on all datasets.

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IJCAI Conference 2023 Conference Paper

Totally Dynamic Hypergraph Neural Networks

Peng Zhou
Zongqian Wu
Xiangxiang Zeng
Guoqiu Wen
Junbo Ma
Xiaofeng Zhu

Recent dynamic hypergraph neural networks (DHGNNs) are designed to adaptively optimize the hypergraph structure to avoid the dependence on the initial hypergraph structure, thus capturing more hidden information for representation learning. However, most existing DHGNNs cannot adjust the hyperedge number and thus fail to fully explore the underlying hypergraph structure. This paper proposes a new method, namely, totally hypergraph neural network (TDHNN), to adjust the hyperedge number for optimizing the hypergraph structure. Specifically, the proposed method first captures hyperedge feature distribution to obtain dynamical hyperedge features rather than fixed ones, by conducting the sampling from the learned distribution. The hypergraph is then constructed based on the attention coefficients of both sampled hyperedges and nodes. The node features are dynamically updated by designing a simple hypergraph convolution algorithm. Experimental results on real datasets demonstrate the effectiveness of the proposed method, compared to SOTA methods. The source code can be accessed via https: //github. com/HHW-zhou/TDHNN.

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IJCAI Conference 2022 Conference Paper

Information Augmentation for Few-shot Node Classification

Zongqian Wu
Peng Zhou
Guoqiu Wen
Yingying Wan
Junbo Ma
Debo Cheng
Xiaofeng Zhu

Although meta-learning and metric learning have been widely applied for few-shot node classification (FSNC), some limitations still need to be addressed, such as expensive time costs for the meta-train and difficult of exploring the complex structure inherent the graph data. To address in issues, this paper proposes a new data augmentation method to conduct FSNC on the graph data including parameter initialization and parameter fine-tuning. Specifically, parameter initialization only conducts a multi-classification task on the base classes, resulting in good generalization ability and less time cost. Parameter fine-tuning designs two data augmentation methods (i. e. , support augmentation and shot augmentation) on the novel classes to generate sufficient node features so that any traditional supervised classifiers can be used to classify the query set. As a result, the proposed method is the first work of data augmentation for FSNC. Experiment results show the effectiveness and the efficiency of our proposed method, compared to state-of-the-art methods, in terms of different classification tasks.

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AIIM Journal 2015 Journal Article

An approach to fuzzy soft sets in decision making based on grey relational analysis and Dempster–Shafer theory of evidence: An application in medical diagnosis

Zhaowen Li
Guoqiu Wen
Ningxin Xie

Objective The existing methods of fuzzy soft sets in decision making are mainly based on different kinds of level soft sets, and it is very difficult for decision makers to select a suitable level soft set in most instances. The goal of this paper is to present an approach to fuzzy soft sets in decision making to avoid selecting a suitable level soft set and to apply this approach to solve medical diagnosis problems. Methods This approach combines grey relational analysis with the Dempster–Shafer theory of evidence. It first utilizes grey relational analysis to calculate the grey mean relational degree, by which we calculate the uncertain degree of various parameters. Then, on the basis of the uncertain degree, the suitable basic probability assignment function of each independent alternative with each parameter can be obtained. Next, we apply Dempster–Shafer rule of evidence fusion to aggregate these alternatives into a collective alternative, by which these alternatives are ranked and the best alternative is obtained. Finally, we compare this approach with the mean potentiality approach. Results The results demonstrate the effectiveness and feasibility of this approach vis-a-vis the mean potentiality approach, Feng's method, Analytical Hierarchy Process and Naive Bayes’ classification method because the measure of performance of this approach is the same as that of the mean potentiality approach, and the belief measure of the whole uncertainty falls from the initial mean 0. 3821 to 0. 0069 in an application of medical diagnosis. Conclusion An approach to fuzzy soft sets in decision making by combining grey relational analysis with Dempster–Shafer theory of evidence is introduced. The advantages of this approach are discussed. A practical application to medical diagnosis problems is given.

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