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They compared the smoothness of the distribution curve between the clustering and k-clustering coefficients. They concluded that the k-clustering coefficient could effectively distinguish clustering degrees in street networks. View article Journal 2022, Physica A: Statistical Mechanics and its Applications...
We introduce new clustering coefficients for weighted networks. They are continuous and robust against edge weight changes. Recently, generalized clustering coefficients for weighted and directed networks have been proposed. These generalizations have a common property, that their values are not continuous....
Here we applied an extended mean-field approach to investigate clustering coefficients in the typical weighted networks proposed by Barrat, Barth\\'elemy and Vespignani (BBV networks). We provide analytical solutions of this model and find that the local clustering in BBV networks depends on the ...
An effective agglomerative technique for clustering large networks was first proposed by [21]. The Girvan-Newman (GN) algorithm [21] first computes the edge-betweenness centrality value of each edge; this is a global metric over the edges and is defined as the number of shortest paths containin...
clustering coefficients [3–5]. There are some trials to create scale-free networks with tunable clustering [6–9], but in these models the desired value of clustering coefficient determines other properties of the networks. Avoiding this problem I wanted to create a model for online social ...
Modeling the optimal design of the future European energy system involves large data volumes and many mathematical constraints, typically resulting in a significant computational burden. As a result, modelers often apply reductions to their model that ca
the higher the average silhouette coefficient. Figure3demonstrates how clustering results depend on the execution time. The box plots show the distribution of average silhouette coefficients for feasible solutions. The execution time is the average of the feasible solutions for each number of steps. Th...
The proposed method is based on the notion that the clustering characteristics of a network can be captured by the combination of nodes’ clustering coefficients and the density of the nodes’ network densities. A community partition is realized by removing all the intercommunity edges in the ...
doi:10.48550/arXiv.1311.6401Vijay K SamalamComputer ScienceSamalam VK (2013) A model for generating tunable clustering coefficients independent of the number of nodes in scale free and random networks [arXiv:1311.6401]