Results 1 to 10 of about 156,516 (263)
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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Bounded graph clustering with graph neural networks
Journal of Physics: ComplexityIn community detection, many methods require the user to specify the number of clusters in advance since an exhaustive search over all possible values is computationally infeasible.
Kibidi Neocosmos +2 more
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Graph Contrastive Clustering [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Recently, some contrastive learning methods have been proposed to simultaneously learn representations and clustering assignments, achieving significant improvements. However, these methods do not take the category information and clustering objective into consideration, thus the learned representations are not optimal for clustering and the ...
Huasong Zhong +7 more
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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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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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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
Matteo Ceccarello +4 more
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A graph clustering algorithm based on a clustering coefficient for weighted graphs [PDF]
Journal of the Brazilian Computer Society, 2010 Abstract Graph clustering is an important issue for several applications associated with data analysis in graphs. However, the discovery of groups of highly connected nodes that can represent clusters is not an easy task. Many assumptions like the number of clusters and if the clusters are or not balanced, may need to be made before the ...
Mariá Cristina Vasconcelos Nascimento +1 more
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Perfectness of clustered graphs [PDF]
Discrete Optimization, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Flavia Bonomo +3 more
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