evaluate_significance: Evaluates significance of cluster algorithm results on a...

In clustAnalytics: Cluster Evaluation on Graphs

Evaluates significance of cluster algorithm results on a graph

Description

Given a graph and a list of clustering algorithms, computes several scoring functions on the clusters found by each of the algorithms.

Usage

evaluate_significance(
  g,
  alg_list = list(Louvain = cluster_louvain, `label prop` = cluster_label_prop, walktrap
    = cluster_walktrap),
  no_clustering_coef = FALSE,
  gt_clustering = NULL,
  w_max = NULL
)

Arguments

g	Graph to be analyzed (as an igraph object)
alg_list	List of clustering algorithms, which take an igraph graph as input and return an object of the communities class.
no_clustering_coef	Logical. If TRUE, skips the computation of the clustering coefficient, which is the most computationally costly of the scoring functions.
gt_clustering	Vector of integers that correspond to labels of the ground truth clustering. The scoring functions will be evaluated on it.
w_max	Numeric. Upper bound for edge weights. Should be generally left as default (NULL).

Value

A data frame with the values of scoring functions (see scoring_functions) of the clusters obtained by applying the clustering algorithms to the graph.

Examples

data(karate, package="igraphdata")
evaluate_significance(karate)

clustAnalytics documentation built on May 29, 2024, 12:18 p.m.