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Integrating Fuzzy Clustering and graph Convolution Network to Accurately Identify Clusters From attributed graph

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 1112-1125页

作者： Yang, Yue Li, Guodong Li, Dongxu Zhang, Jun Hu, Pengwei Hu, Lun Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830000 Peoples R China

attributed graph clustering is of significance for an in-depth understanding of the intrinsic organization of complex networks. Recently, owing to the powerful learning capability of deep neural networks, increasing attention has been paid to developing more advanced deep learning approaches for attributed graph clustering. However, these approaches mainly focus on the purpose of obtaining general node representations, followed by clustering as a downstream task in combination with traditional techniques. Intuitively, the clustering performance is further limited due to the lack of achieving the desirable clustering quality in the graph representation learning process. To this end, this paper proposes a novel end-to-end attributed graph clustering model, namely FCGCN, by integrating fuzzy clustering and graph convolution network. FCGCN is trained toward optimizing an unsupervised fuzzy-based clustering objective, which is specifically formulated for precisely evaluating the clustering quality by considering the fuzzy memberships of nodes over clusters. To avoid the generation of undesirable clusters, we introduce a tailored regularization term by alleviating the over-smoothing issue on graph neural networks. By doing so, an explicit connection between graph representation learning and clustering can thus be established, considerably improving the clustering performance. To evaluate the performance of FCGCN, extensive experiments are conducted on a total of five real attributed graph datasets, and the results demonstrate the superiority of FCGCN over several state-of-the-art algorithms in terms of effectiveness and efficiency.

关键词： Clustering algorithms Representation learning graph neural networks Optimization Convolution Clustering methods Feature extraction Deep learning Loss measurement Data mining attributed graph fuzzy clustering graph representation graph neural network

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Detecting and Untangling Composite Commits via attributed graph Modeling

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JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 2025年第1期40卷 119-137页

作者： Xu, Sheng-Bin Chen, Si-Yu Yao, Yuan Xu, Feng Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

During software development, developers tend to tangle multiple concerns into a single commit, resulting in many composite commits. This paper studies the problem of detecting and untangling composite commits, so as to improve the maintainability and understandability of software. Our approach is built upon the observation that both the textual content of code statements and the dependencies between code statements are helpful in comprehending the code commit. Based on this observation, we first construct an attributed graph for each commit, where code statements and various code dependencies are modeled as nodes and edges, respectively, and the textual bodies of code statements are maintained as node attributes. Based on the attributed graph, we propose graph-based learning algorithms that first detect whether the given commit is a composite commit, and then untangle the composite commit into atomic ones. We evaluate our approach on nine C# projects, and the results demonstrate the effectiveness and efficiency of our approach.

关键词： composite commit commit untangling code dependency graph attributed graph

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Weighted symmetric nonnegative matrix factorization and graph-boosting to improve the attributed graph clustering

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 142卷

作者： Li, Shunlei Wan, Lili Zhang, Yin Luo, Lixia Chinese Univ Hong Kong Multiscale Med Robot Ctr Ltd Hong Kong Peoples R China Wuhan Donghu Univ Sch Mech & Elect Engn Wuhan 430212 Hubei Peoples R China Wuhan Polytech Univ Sch Elect & Elect Engn Wuhan 430023 Hubei Peoples R China Guilin Univ Elect Technol Sch Optoelect Engn Guilin 541004 Guangxi Peoples R China Hunan Univ Informat Technol Sch Comp Sci & Engn Changsha 410148 Hunan Peoples R China

graph clustering is a critical task in network analysis, aimed at grouping nodes based on their structural or attributed similarities. In particular, attributed graph clustering, which considers both structural links and node attributes, is essential for complex networks where additional node information is available. Nonnegative Matrix Factorization (NMF) has shown promise in graph clustering;however, it faces limitations when applied to attributed graph clustering, such as an inability to detect outliers, distortion of geometric data point structures, and disregard for attributed information. Moreover, many existing attributed graph clustering methods overlook distant node relationships due to network sparsity, which hinders further performance improvements. To address these challenges, this paper introduces Weighted Symmetric NMF with graph-Boosting for attributed graph Clustering (WSBGC), an innovative extension of NMF. WSBGC leverages attribute similarity among nodes to create a weighted version of NMF, enabling outlier detection while preserving the geometric structure of data points. Additionally, WSBGC employs graph-boosting, leveraging attribute information to account for distant node relationships and improve clustering accuracy. A graph attention auto-encoder is then used to construct the final clustering model. The effectiveness of WSBGC is validated through extensive experiments on real-world datasets. Notably, our algorithm improves accuracy by 2.5% compared to the best available method, demonstrating its superior clustering performance in attributed graphs.

关键词： attributed graph graph clustering graph-boosting Nonnegative matrix factorization Weighted symmetric

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Dual-Scale attributed graph Transformer for Extracting Spatial-Temporal Features With Applications in Quality Index Prediction

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1873-1884页

作者： Zhang, Kesheng Yu, Wen Chai, Tianyou Northeastern Univ Key Lab Integrated Automat Proc Ind Shenyang 110004 Peoples R China Natl Polytech Inst Dept Control Automat CINVES TAVIPN Mexico City 07360 Mexico

This paper presents a novel deep learning architecture, the Dual-scale Attribute graph Transformer (DAGT), for extracting spatial-temporal features from attributed graph data. DAGT addresses the challenge of inconsistent sampling periods in industrial data streams by utilizing two key modules: 1) Dual-Scale Spatial-temporal graph Convolution Network (DSGCN): This module captures both spatial and temporal information within attributed graphs, enabling effective feature extraction for tasks like quality index prediction. 2) Spatial-temporal graph Attention Block (SGAB): This module employs an attention mechanism to selectively focus on crucial areas of the graph sequence. By assigning higher weights to regions with significant spatial-temporal features, SGAB refines the feature representation. The contributions of DAGT lie in the construction of a dual-scale adjacency matrix for efficient temporal and spatial dimensionality reduction and the design of a graph pooling module via spatial clustering. These innovations enhance the model's ability to learn from attributed graph sequences. The proposed method for quality index prediction is validated using real-world industrial data of the mineral processing process and various comparative experiments.

关键词： Indexes Production Feature extraction Time series analysis Convolution Process control Data mining Correlation Transformers Topology attributed graph dual-scale graph convolution networks spatial-temporal features quality index

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attributed graph mining in the presence of automorphism

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KNOWLEDGE AND INFORMATION SYSTEMS 2017年第2期50卷 569-584页

作者： Pasquier, Claude Flouvat, Frederic Sanhes, Jeremy Selmaoui-Folcher, Nazha Univ Nice Sophia Antipolis I3S UMR 7271 F-06900 Sophia Antipolis France CNRS I3S UMR 7271 F-06900 Sophia Antipolis France Univ New Caledonia Multidisciplinary Res Team Mat & Environm PPME Noumea 98851 New Caledonia

attributed directed graphs are directed graphs in which nodes are associated with sets of attributes. Many data from the real world can be naturally represented by this type of structure, but few algorithms are able to directly handle these complex graphs. Mining attributed graphs is a difficult task because it requires combining the exploration of the graph structure with the identification of frequent itemsets. In addition, due to the combinatorics on itemsets, subgraph isomorphisms (which have a significant impact on performances) are much more numerous than in labeled graphs. In this paper, we present a new data mining method that can extract frequent patterns from one or more directed attributed graphs. We show how to reduce the combinatorial explosion induced by subgraph isomorphisms thanks to an appropriate processing of automorphic patterns.

关键词： attributed graph Frequent pattern mining Automorphism Structure mining Itemset mining

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attributed graph Matching via Seeds Guiding

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2021年第5期16卷 761-768页

作者： Zhu, Hui Zheng, Yali Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

attributed graph is a typical class of graphs with a set of attributes on nodes, edges, even high-order structure. Objects in the fields are naturally represented as attributed graphs with considerable noise and a large number of nodes, which make traditional graphs matching techniques confront with some new challenges, such as optimization difficulty, inability to match and so on. In order to solve these problems, we propose a new approach, called attributed graph Matching via Seeds Guiding (AGM-SG) in this paper. Our approach introduces seed nodes to guide attributed graph matching, and considers explicitly the first-order characteristics difference in the problem formulation. It is formulated as a quadratic optimization, and solved by Frank-Wolfe algorithm with continuous relaxation. It only has O(n(2)) space complexity, and is suitable to apply to super-large graphs under different types. We evaluate the proposed approach on the synthetic dataset and three different real datasets including Wikipedia dataset, Enron mail dataset, and Caenorhabditis elegans dataset. Compared with the existing graph matching algorithms, the proposed algorithm outperforms original SGM, RGM and AGMLG significantly, which has more than 5% matching accuracy improvement on all datasets. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： attributed graph graph matching vertex alignment

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attributed graph clustering under the contrastive mechanism with cluster-preserving augmentation

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INFORMATION SCIENCES 2024年 681卷

作者： Zheng, Yimei Jia, Caiyan Yu, Jian Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

attributed graph clustering is a fundamental task in complex network analysis. Many existing graph clustering methods utilize graph representation learning techniques to learn node representations, subsequently applying K-means for clustering. Despite the significant attention and promising outcomes of graph contrastive learning in graph representation learning, we find two essential problems that need to be solved. (1) How to achieve augmentation for contrast that preserves the cluster structure of a given graph? (2) How to design an effective contrastive learning mechanism that collaborates with clustering? Therefore, we propose an attributed graph clustering method under the contrastive mechanism with cluster-preserving augmentation, integrating node representation learning and clustering into a unified framework. Specifically, we construct a contrasting view based on a generated kNN graph and edge betweenness centrality to preserve the cluster structure in the original graph. Meanwhile, a multilevel contrast mechanism based on pseudo-label-guided negative sampling is proposed to maximize the agreement between node representations in multiple latent spaces. We jointly optimize a specific clustering objective during the contrastive process, leading to refined cluster distribution directly in training. Extensive experiments on several datasets demonstrate that our proposed model consistently outperforms existing state-of-the-art methods on clustering.

关键词： graph augmentation Contrastive learning attributed graph Deep embedded clustering

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attributed graph Subspace Clustering with graph-Boosting 15

Attributed Graph Subspace Clustering with Graph-Boosting

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15th Asian Conference on Machine Learning (ACML)

作者： Li, Wang Zhu, En Wang, Siwei Guo, Xifeng Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China Dongguan Univ Technol Sch Cyberspace Sci Dongguan 523808 Guangdong Peoples R China

attributed graph clustering groups nodes into disjoint categories with graph convolutional networks and has exhibited promising performance in various applications. However, there are two issues preventing the performance from being improved further. First, the relationships between distant nodes are generally overlooked due to the sparsity of graphs. Second, the graph convolutional networks with few layers are sensitive to noises. To address these issues, we propose attributed graph Subspace clustering with graph-Boosting (AGSGB). Specifically, to deal with the first issue, an auxiliary graph is built from the feature matrix to establish the distant relationships. And to address the second issue, a subspace clustering module, famous for its robustness to noise, is introduced. Based on the auxiliary graph and the subspace clustering module, a graph enhance module and a graph refine module are constructed, together with the graph autoencoder constituting the final clustering model. By using the given graph and the refined graph built by the graph refine module, a dual guidance supervisor is designed to train the clustering model. Finally, the clustering result can be obtained by the subspace clustering module. Extensive experimental results on five public benchmark datasets validate the effectiveness of the proposed method.

关键词： attributed graph graph auto-encoder graph boosting subspace clustering

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A survey of typical attributed graph queries

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2021年第1期24卷 297-346页

作者： Wang, Yanhao Li, Yuchen Fan, Ju Ye, Chang Chai, Mingke Univ Helsinki Dept Comp Sci Helsinki 00560 Finland Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore Renmin Univ China Key Lab Data Engn & Knowledge Engn DEKE Beijing 100872 Peoples R China Renmin Univ China Informat Sch Beijing 100872 Peoples R China

graphs are commonly used for representing complex structures such as social relationships, biological interactions, and knowledge bases. In many scenarios, graphs not only represent topological relationships but also store the attributes that denote the semantics associated with their vertices and edges, known as attributed graphs. attributed graphs can meet demands for a wide range of applications, and thus a variety of queries on attributed graphs have been proposed. However, these diverse types of attributed graph queries have not been systematically investigated yet. In this paper, we provide an extensive survey of several typical types of attributed graph queries. We propose a taxonomy of attributed graph queries based on query inputs and outputs. We summarize the definitions of queries that fall into each category and present a fine-grained classification of queries within each category by analyzing the semantics and algorithmic motivations behind these queries. Moreover, we discuss the insights of how existing studies address the technical challenges of query processing and outline several promising future research directions.

关键词： attributed graph Knowledge base Query definition Query processing Taxonomy Survey

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A fuzzy clustering based method for attributed graph partitioning

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第9期10卷 3399-3407页

作者： He, Chaobo Liu, Shuangyin Zhang, Lei Zheng, Jianhua Zhongkai Univ Agr & Engn Sch Informat Sci & Technol Guangzhou Guangdong Peoples R China

graph partitioning methods in data mining have been widely used to discover protein complexes in protein-protein interaction (PPI) network. However, PPI networks with attributes need more effective attribute graph partitioning methods. Attribute graph partitioning aims to obtain high quality partitions satisfying the requirement: nodes in the same partition not only connect to each other more densely but also share more similar attribute values. In this paper, we propose a novel method for attributed graph partitioning based on fuzzy clustering. This method firstly devises a unified similarity measure using SimRank to construct the fuzzy similarity matrix of the attributed graph and can integrate structural and attribute similarities of nodes into a flexible weighted framework. Then it deduces the corresponding fuzzy equivalent matrix using fuzzy set theory. Finally, the result of partitioning can be obtained using fuzzy clustering algorithm. We conduct some experiments on several typical attributed graphs, which can also simulate PPI networks with attributes. The results show that our method is very effective to identify high quality partitions of attributed graphs and even performs better than some representative methods.

关键词： attributed graph graph partitioning Fuzzy clustering SimRank Protein-protein interaction network

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