Results 11 to 20 of about 178,314 (358)
Clustering Uncertain Graphs [PDF]
Proceedings of the VLDB Endowment, 2016 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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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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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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AutoGraph: Autonomous Graph Based Clustering of Small-Molecule Conformations
, 2020 While accurately modeling the conformational ensemble is required for predicting properties of flexible molecules, the optimal method of obtaining the conformational ensemble seems as varied as their applications.
Kiyoto Aramis Tanemura +2 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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Data & Knowledge Engineering, 2022 In application domains ranging from social networks to e-commerce, it is important to cluster users with respect to both their relationships (e.g., friendship or trust) and their actions (e.g., visited locations or rated products). Motivated by these applications, we introduce here the task of clustering the nodes of a sequence graph, i.e., a graph ...
H. Zhong (Haodi) +2 more
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Graph Clustering with Graph Neural Networks
J. Mach. Learn. Res., 2020 Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs.
Anton Tsitsulin +3 more
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Contrastive and attentive graph learning for multi-view clustering
, 2022 Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering.
Li, Lin +4 more
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A survey of kernel and spectral methods for clustering [PDF]
, 2007 Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F. +11 more
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