Cluster Evaluation on Graphs
Conductance
Computes possible membership vectors from contingency table
Natural logarithm of the number of contingency tables
Coverage
Cut Ratio
Density Ratio
Edges Inside
Estimates |H_0|/|H_r*|
Estimates |H_i|/|H_{i+1}| for the first r rows
Evaluates significance of cluster algorithm results on a graph
Applies function to each subgraph of a graph
Auxiliary Functions of a Graph Partition
Average Degree
Average Out Degree Fraction
Generates a Barabási-Albert graph with community structure
Performs nonparametric bootstrap to a graph and a list of clustering a...
Contingency table from membership vectors
clustAnalytics: Cluster Evaluation on Graphs
Evaluates the significance of a graph's clusters
Expansion
FOMD (Fraction Over Median Degree)
Estimates |H_i|/|H_(i+1)| for the first n_rows rows
Returns edgelist with weights from a weighted igraph graph
Internal Density
Approximation of log(omega(a,b))
Make graph weighted
Max Out Degree Fraction
Normalized cut
Maximum, Average, and Flake Out Degree Fractions of a Graph Partition
Reduced Mutual Information
Relabels membership vector
Randomizes a weighted graph while keeping the degree distribution cons...
Scoring Functions of a Graph Partition
Sort matrix
Triangle Participation Ratio (community-wise)
Performs a step of the Markov Chain Monte Carlo method
Weighted clustering coefficient of a weighted graph.
Weighed transitivity of a weighted graph.
Evaluates the stability and significance of clusters on 'igraph' graphs. Supports weighted and unweighted graphs. Implements the cluster evaluation methods defined by Arratia A, Renedo M (2021) <doi:10.7717/peerj-cs.600>. Also includes an implementation of the Reduced Mutual Information introduced by Newman et al. (2020) <doi:10.1103/PhysRevE.101.042304>.
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