Results 11 to 20 of about 242,831 (264)
Approximating Clustering Coefficient and Transitivity
Journal of Graph Algorithms and Applications, 2005 Since its introduction in the year 1998 by Watts and Strogatz, the clustering coefficient has become a frequently used tool for analyzing graphs. In 2002 the transitivity was proposed by Newman, Watts and Strogatz as an alternative to the clustering
Thomas Schank, Dorothea Wagner
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A Cluster Character with Coefficients for Cluster Category [PDF]
Algebras and Representation Theory, 2021 18 ...
Fernando Borges, Tanise Carnieri Pierin
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Relative Clustering Coefficient
CoRR, 2021 In this paper, we relatively extend the definition of global clustering coefficient to another clustering, which we call it relative clustering coefficient. The idea of this definition is to ignore the edges in the network that the probability of having an edge is 0.
Farahbakhsh Touli, Elena +1 more
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Clustering Coefficients for Correlation Networks [PDF]
Frontiers in Neuroinformatics, 2018 5 figures, 2 tables; abstract is made shorter than in the journal version due to the length ...
Naoki Masuda +4 more
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Clustering coefficients of large networks [PDF]
Information Sciences, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yusheng Li 0001 +2 more
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Spatially clustered varying coefficient model
Journal of Multivariate Analysis, 2022 59 pages, 5 ...
Fangzheng Lin +3 more
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On Learning Cluster Coefficient of Private Networks [PDF]
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012 Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data.
Yue Wang 0009 +3 more
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Efficient computation of the Weighted Clustering Coefficient [PDF]
Internet Mathematics, 2014 The clustering coefficient of an unweighted network has been extensively used to quantify how tightly connected is the neighbor around a node and it has been widely adopted for assessing the quality of nodes in a social network. The computation of the clustering coefficient is challenging since it requires to count the number of triangles in the graph.
LATTANZI, SILVIO, LEONARDI, Stefano
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Brain Sciences, 2021 We aimed to evaluate diffusion tensor imaging (DTI) in infants born extremely preterm, to determine the effect of erythropoietin (Epo) on DTI, and to correlate DTI with neurodevelopmental outcomes at 2 years of age for infants in the Preterm ...
Janessa B. Law +7 more
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Influence of clustering coefficient on network embedding in link prediction
Applied Network Science, 2022 Multiple network embedding algorithms have been proposed to perform the prediction of missing or future links in complex networks. However, we lack the understanding of how network topology affects their performance, or which algorithms are more likely ...
Omar F. Robledo +3 more
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