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Auxiliary Graph for Attribute Graph Clustering [PDF]
Entropy, 2022 Attribute graph clustering algorithms that include topological structural information into node characteristics for building robust representations have proven to have promising efficacy in a variety of applications.
Wang Li +4 more
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Soft graph clustering for single-cell RNA sequencing data [PDF]
BMC BioinformaticsBackground Clustering analysis is fundamental in single-cell RNA sequencing (scRNA-seq) data analysis for elucidating cellular heterogeneity and diversity.
Ping Xu +5 more
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Clustering Method Based on Contrastive Learning for Multi-relation Attribute Graph [PDF]
Jisuanji kexue, 2023 In the real world,there are many complex graph data which includes multiple relations between nodes,namely multi-relation attribute graph.Graph clustering is one of the approaches for mining similar information from graph data.However,most existing graph
XIE Zhuo, KANG Le, ZHOU Lijuan, ZHANG Zhihong
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Graph Clustering Algorithm Based on Node Clustering Complexity [PDF]
Jisuanji kexue, 2023 Graph clustering is an important task in the analysis of complex networks,which can reveal the community structure within a network.However,clustering complexity of nodes varies throughout the network.To address this issue,a graph clustering algorithm ...
ZHENG Wenping, WANG Fumin, LIU Meilin, YANG Gui
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Clustering uncertain graphs [PDF]
Proceedings of the VLDB Endowment, 2017 An uncertain graph 𝒢 = (V, E, p : E → (0, 1]) can be viewed as a probability space whose outcomes (referred to as possible worlds ) are subgraphs of 𝒢 where any edge e ε E occurs with probability p
Ceccarello, Matteo +4 more
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Attribute Graph Clustering Based on Self-Supervised Spectral Embedding Network
IEEE Access, 2023 Attribute graph clustering requires joint modeling of both graph structure and node properties, which is challenging. In recent years, graph neural networks have been utilized to mine deep information on attribute graphs through feature aggregation ...
Xiaolin Ning +3 more
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Robust Spectral Clustering Incorporating Statistical Sub-Graph Affinity Model
Axioms, 2022 Hyperspectral image (HSI) clustering is a challenging work due to its high complexity. Subspace clustering has been proven to successfully excavate the intrinsic relationships between data points, while traditional subspace clustering methods ignore the ...
Zhenxian Lin, Jiagang Wang, Chengmao Wu
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Graph Learning for Attributed Graph Clustering
Mathematics, 2022 Due to the explosive growth of graph data, attributed graph clustering has received increasing attention recently. Although deep neural networks based graph clustering methods have achieved impressive performance, the huge amount of training parameters ...
Xiaoran Zhang, Xuanting Xie, Zhao Kang
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Discrete Optimal Graph Clustering [PDF]
IEEE Transactions on Cybernetics, 2020 Graph based clustering is one of the major clustering methods. Most of it work in three separate steps: similarity graph construction, clustering label relaxing and label discretization with k-means. Such common practice has three disadvantages: 1) the predefined similarity graph is often fixed and may not be optimal for the subsequent clustering.
Yudong Han +4 more
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An Efficient Vertex-Driven Temporal Graph Model and Subgraph Clustering Method
IEEE Access, 2022 The temporal graph can represent a temporal relationship widely used in compound synthesis analysis, biological gene analysis, etc. However, the temporal graph would embody vertex updates frequently, high time resolution, and not enumerated rules.
Hanlin Zhang +4 more
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