getOCG.clusters: Generate Overlapping Cluster Generator (OCG) Communities

In linkcomm: Tools for Generating, Visualizing, and Analysing Link Communities in Networks

Description

This function generates communities based on the OCG algorithm.

Usage

getOCG.clusters(network, init.class.sys = 3, max.class.card = 0, 
                cent.class.sys = 1, min.class = 2, verbose = TRUE, keep.out = FALSE)

Arguments

network	Either a character string naming the file containing the network as an edge list, or a data frame/matrix object containing the edge list.
init.class.sys	An integer number specifying the Initial Class System: 1 - Maximal Cliques, 2 - Edges, or 3 - Centered Cliques. Defaults to 3.
max.class.card	An integer number specifying the maximum allowed class cardinality. Defaults to 0, which indicates no constraint.
cent.class.sys	A binary value indicating the choice of class system for centered cliques: 0 - Final class system, needs the expected minimum number of clusters and the maximum caldinality of the final clusters, or 1 - the class system that maximizes modularity. Defaults to 1.
min.class	An integer number specifying the minimum number of expected classes. Defaults to 2.
verbose	Logical, whether to display progress of the algorithm to the screen. Defaults to TRUE.
keep.out	Logical, whether to keep the OCG partition intermediate file on disk or not. Defaults to FALSE.

Value

An object of class OCG, which is a list containing the following elements:

numbers	An integer vector with the number of edges, nodes, and communities.
modularity	An integer number specifying the modularity of the network.
Q	A real number specifying the value of Q generated by the OCG algorithm.
nodeclusters	A data frame consisting of 2 columns; the first contains node names, and the second contains single community IDs for each node. All communities and their nodes are represented, but not necessarily all nodes.
numclusters	A named integer vector. Names are node names and integer values are the number of communities to which each node belongs.
igraph	An object of class igraph. The network is represented here as an igraph object.
edgelist	A character matrix with 2 columns containing the nodes that interact with each other.
clustsizes	A named integer vector. Names are community IDs and integer values indicate the number of nodes that belong in each community.

Note

For optimal results, the input network must contain at least one connected component (a subgraph in which any two vertices are connected by a path, which is not connected to additional vertices in the supergraph).

Author(s)

Alain Guenoche (main algorithm), and ported into R by Alex T. Kalinka [e-mail]

References

Becker, E., Robisson, B., Chapple, C.E., Guenoche, A. and Brun, C. (2012) Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics 28, 84-90.

Examples

## Generate graph and extract OCG communities.
g <- swiss[,3:4]
oc <- getOCG.clusters(g)

Example output

Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

Loading required package: RColorBrewer

Welcome to linkcomm version 1.0-11

For a step-by-step guide to using linkcomm functions:
   > vignette(topic = "linkcomm", package = "linkcomm")
To run an interactive demo:
   > demo(topic = "linkcomm", package = "linkcomm")
To cite, see:
   > citation("linkcomm")
NOTE: To use linkcomm, you require read and write permissions in the current directory (see: help("getwd"), help("setwd"))

Calculating Initial class System....Done
Nb. of classes 21
Nb. of edges not within the classes 11
Number of initial classes 21
Running....
Remaining classes: 20 of 21     
Remaining classes: 10 of 21     
Remaining classes: None                 
Reading OCG data...
Extracting cluster sizes... 5% 
Extracting cluster sizes... 11% 
Extracting cluster sizes... 17% 
Extracting cluster sizes... 23% 
Extracting cluster sizes... 29% 
Extracting cluster sizes... 35% 
Extracting cluster sizes... 41% 
Extracting cluster sizes... 47% 
Extracting cluster sizes... 52% 
Extracting cluster sizes... 58% 
Extracting cluster sizes... 64% 
Extracting cluster sizes... 70% 
Extracting cluster sizes... 76% 
Extracting cluster sizes... 82% 
Extracting cluster sizes... 88% 
Extracting cluster sizes... 94% 
Extracting cluster sizes... 100% 

linkcomm documentation built on Feb. 4, 2021, 5:05 p.m.