Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an ...
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...
The clustering coefficient is defined as the probability that two neighboring vertices of a given vertex are also neighbors of each other, and may provide another useful feature to characterize instance difficulty for graph based problems like timetabling. ...
GraphTheory GlobalClusteringCoefficient compute the global clustering coefficient Calling Sequence Parameters Description Examples Compatibility Calling Sequence GlobalClusteringCoefficient( G ) Parameters G - graph Description GlobalClusteringCoefficien
The square (Euclidean) distance, city block distance, correlation coefficient, and hamming distance are some common attribute dissimilarity functions (Murphy, 2012). For the k-means clustering algorithm in which k initial points are selected to represent the initial cluster centers, all data points ...
Min-hash approach:Min-hash approach attempts to define a node’s outlinks (hyperlinks) as sets, i.e. two nodes are considered similar, if they share many outlinks [73]. The jaccard coefficient is used to represent the similarity between two nodes (Baharav et al. [74]). ...
For each data object, the sparse representation coefficient vector is computed by sparse representation theory and KNN algorithm is used to find the k nearest neighbors. Instead of using all the coefficients to construct the affinity matrix directly, we update each coefficient vector by remaining ...
(Additional file1: Fig. S11b), the configurations of 18 and 8 neighbors correspond to two-hop neighbors in graph theory. For STARmap and osmFISH technologies, which lack patterned unit organization, we performed ablation studies and found that 8 neighbors achieved the best ARI scores of 0.73 ...
1A). Figure 1B demonstrates that the high clustering coefficient gene nodes in the GCN of the resistant cassava varieties (GCN-RC) tended to be highly connected to others, thus possessing high node degree value). The difference in the network topology of both cultivars under control conditions ...
4th panel). Similarity may be measured by metrics such as Pearson correlation coefficient. An example of a clustering algorithm is the hierarchical clustering [46,62], which builds adendrogramof all samples. Other examples are K-means [65], c-means [66], spectral-based [67], and graph-bas...