alc: Average local clustering (alc) coefficient

In memnet: Network Tools for Memory Research

Description

Computes the uncorrected or corrected average local clustering coffiecient.

Usage

alc(adj, types = "uncorrected", weights = NULL, mode = "undirected")

Arguments

adj	numeric matrix representing the adjacency matrix.
types	character. Either "uncorrected" or "corrected", or a vector containing both.
weights	numeric vector of edge weights. Optional.
mode	character, either "directed" or "undirected", specifying whether the network should be interepeted as directed or undirected. Defaults to "undirected".

Details

The uncorrected clustering coefficent is computed according to Watts & Strogatz (1998). The corrected clustering coefficient normalizes the uncorrected one by the average degree / n nodes, i.e., the expected average local clustering for an Erdös-Renyi random graph.

Value

the corrected local clustering coefficient and/or the uncorrected clustering coefficient.

Examples

# get fluency data
data(animal_fluency)

# edge lists of fluency graphs
edge_list = threshold_graph(animal_fluency)

# get adjacency matrices
adj = edg_to_adj(edge_list)

# get local average clustering coefficient
alc(adj)

# get corrected local average clustering coefficient
alc(adj, types = 'corrected')

memnet documentation built on May 2, 2019, 9:35 a.m.