scoring_functions {clustAnalytics}

Scoring Functions of a Graph Partition

Description

Computes the scoring functions of a graph and its clusters.

Usage

scoring_functions(
  g,
  com,
  no_clustering_coef = TRUE,
  type = "local",
  weighted = TRUE,
  w_max = NULL
)

Arguments

g

Graph to be analyzed (as an igraph object). If the edges have a "weight" attribute, those will be used as weights (otherwise, all edges are assumed to be 1).

com

Community membership integer vector. Each element corresponds to a vertex of the graph, and contains the index of the community it belongs to.

no_clustering_coef

Logical. If TRUE, skips the computation of the clustering coefficient (which can be slow on large graphs).

type

can be "local" for a cluster by cluster analysis, or "global" for a global analysis of the whole graph partition.

weighted

Is the graph weighted? If it is, doesn't compute TPR score.

w_max

Numeric. Upper bound for edge weights. Should be generally left as default (NULL). Only affects the computation of the clustering coefficient.

Value

If type=="local", returns a dataframe with a row for each community, and a column for each score. If type=="global", returns a single row with the weighted average scores.

See Also

Other cluster scoring functions: FOMD(), average_degree(), average_odf(), conductance(), coverage(), cut_ratio(), density_ratio(), edges_inside(), expansion(), internal_density(), max_odf(), normalized_cut(), weighted_clustering_coefficient(), weighted_transitivity()

Examples

data(karate, package="igraphdata")
scoring_functions(karate, membership(cluster_louvain(karate)))