Clustering coefficientLarge dynamic graphsNode rankingEfficiently searching top- k representative vertices is crucial for understanding the structure of large dynamic graphs. Recent studies show that communities
The clustering coefficient is a measure that indicates the level of cohesion in the neighborhood of a node in a network. It can be divided into local values, which measure the cohesion around a specific node, and global values, which measure the clusters of the entire network. ...
In this paper, we propose a structured encryption scheme to achieve privacy-preserving local clustering coefficient query (STE-CC) on the outsourced encrypted graphs. To calculate the clustering coefficient, we design the PSIsum protocol to sum the number of intersections, in which the basic ...
() Citation Context ...ity, connected to the clustering coefficient, has been recently shown to influence directly the formation of strongly connected sub... AP Quayle,AS Siddiqui,SJM Jones - 《European Physical Journal B》 被引量: 35发表: 2006年 Applying the grey assessment to the evaluation...
(FCM), which was first introduced by Dunn (1973) and Bezdek (1974,1981), is considered the most representative one and is still widely used today. In this formulation, the “fuzziness" is obtained by raisingun,cto an exponentm>1, serving as the fuzziness coefficient regulating the extent ...
As a result, it can assign a community label to the node by determining the index of the biggest element in the coefficient matrix, and the NMF detection rule is: $$\begin{aligned} \text {community}(v_j) = \arg \max _{i=1,2,\dotsc ,V_{j1}} \end{aligned}$$ (3) Different...
The range of the fusion values for the resulting hierarchy is not between 1 and 0, as you would expect for the matching coefficient. The conclusions from the cluster analysis, however, agree well with the results obtained in other ways. Stata does not restrict your choice of similarity or ...
We found that, while the CC heatmaps show four crisp clusters (Fig. 1a– b, Supplementary Note 1), the appearance of clusters in the Pearson's correlation coefficient matrix (Fig. 1c) is much weaker, and principal component analysis (PCA) does not show discernible gaps among reported ...
measured in terms of theirclustering. In the literature, three different measures for clustering are often considered. The first is the so-called global clustering coefficient, defined as three times the ratio of the number of triangles to the number of paths of length two in the graph. The ...
Clustering structure of the dataset is measured by the divisive coefficient. For each learning example ti, d(i) is defined as the diameter of the last cluster to which it belongs (before being split off as a single observation), divided by the diameter of the whole dataset. The divisive co...