In graph theory, the clustering coefficient (also known as clustering coefficient, clustering coefficient) is the coefficient used to describe the degree of clustering between the vertices of a graph. Specifically, it is the degree to which the adjacent points of a point are connected to each oth...
Considering the clustering coefficient of the network, we analytically investigate the existence and the local asymptotic stability of each equilibrium of these models and derive threshold values for the prevalence of diseases. Additionally, we obtain two equivalent epidemic thresholds in dynamic networks,...
5.3.1.1.4Clustering coefficient Theclustering coefficientis used to explain the network connectivity. It is a metric of the degree to find the nodes in a network that cluster together. There are two ways to represent the measure of clustering coefficient:global clustering coefficient and local clust...
Batagelj, V. (2016). Corrected overlap weight and clustering coefficient. In Proceedings of the INSNA International Social Network Conference, pages 16-17, Newport Beach, CA, USA.Batagelj, Vladimir. (2016). Corrected overlap weight and clustering coefficient. Pages 16-17 of: Proceedings of the ...
These results are used to present a lower and an upper bounds for the clustering coefficient and the diameter of the given edge number expectation generalized small-world network, respectively. In other words, we prove mathematically that the given edge number expectation generalized small-world ...
摘要: SynonymsSynonymsCliquishness; Density of a subgraph; Transitivity; Watts-Strogatz local clustering coefficientDefinitionDefinitionMany problems in network analysis converge to the question of the cohe被引量: 8 年份: 2013 收藏 引用 批量引用 报错 分享 ...
In binary directed networks, the clustering coefficient of node i for a binary network may be defined as the ratio between all the possible triangles formed by i and the number of all possible triangles that could be formed CiD (A) = dtot i 2[ (A + AT )3 ii . (dtot − 1 ...
First-principle network models are crucial to understanding the intricate topology of real complex networks. Although modelling efforts have been quite successful in undirected networks, generative models for networks with asymmetric interactions are sti
5, the message can capture a larger population again on the RLs, despite of the existence of hubs the short characteristic path length in the SF (ER) networks. The reason is that very smaller clustering coefficient gives rise to weak social reinforcement effect3,31, which again leads to ...
Jothi N, Nur’aini Abdul Rashidb WH (2015) Data mining in healthcare—a review. Procedia Comput Sci 72:306–313 Google Scholar Kalgotra P, Sharda R, Luse A (2020) Which similarity measure to use in network analysis: Impact of sample size on phi correlation coefficient and Ochiai index....