# clusteringCoefficient-methods: Clustering coefficient of a graph In Bioconductor/graph: graph: A package to handle graph data structures

## Description

This generic function takes an object that inherits from the `graph` class. The graph needs to have `edgemode=="undirected"`. If it has `edgemode=="directed"`, the function will return NULL.

## Usage

 ```1 2``` ```## S4 method for signature 'graph' clusteringCoefficient(object, selfLoops=FALSE) ```

## Arguments

 `object` An instance of the appropriate graph class. `selfLoops` Logical. If true, the calculation takes self loops into account.

## Details

For a node with n adjacent nodes, if `selfLoops` is `FALSE`, the clustering coefficent is N/(n*(n-1)), where N is the number of edges between these nodes. The graph may not have self loops. If `selfLoops` is `TRUE`, the clustering coefficent is N/(n*n), where N is the number of edges between these nodes, including self loops.

## Value

A named numeric vector with the clustering coefficients for each node. For nodes with 2 or more edges, the values are between 0 and 1. For nodes that have no edges, the function returns the value NA. For nodes that have exactly one edge, the function returns NaN.

## Author(s)

Wolfgang Huber http://www.dkfz.de/mga/whuber

## Examples

 ```1 2 3 4``` ```set.seed(123) g1 <- randomGraph(letters[1:10], 1:4, p=.3) clusteringCoefficient(g1) clusteringCoefficient(g1, selfLoops=TRUE) ```

Bioconductor/graph documentation built on May 31, 2021, 8:28 p.m.