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...
In this paper, we investigate how the clustering coefficient of a network, i.e., the probability that the neighbours of a node are also connected, affects network embedding algorithms' performance in link prediction, in terms of the AUC (area under the ROC curve). We evaluate classic ...
This approach detects the community of strongly mutually connected components having different values of the relation residing in the network. Experimental results demonstrated that components having a higher value of clustering coefficient are strongly connected and form a community of mutual connectivity. ...
The clustering coefficient of node i can be computed by Ci=2Eiki(ki−1) where ki is the number of the directed neighbors of node i, and Ei is the number of actual connections among these ki nodes. In [47], the clustering coefficient of the China Railway Network was calculated, and...
Synonyms Cliquishness ; Density of a subgraph ; Transitivity ; Watts-Strogatz local clustering coefficient Definition Many problems in network analysis converge to the question of the cohesion of a graph, aiming to determine the extent to which the nodes of a network are closely connected with one...
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 ...
Cascading failure is ubiquitous in many networked infrastructure systems, such as power grids, Internet and air transportation systems. In this paper, we extend the cascading failure model to a scale-free network with tunable clustering and focus on the effect of clustering coefficient on system robu...
in or between local-worlds,and introduced triad formation to tune the clustering.The MATLAB and C++ simu-lation indicates that not only the degree distribution consistent with the theoretical results,but also the clustering coefficient had a good performance than the local-world evolving network,and ...
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 ...