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Last data update: 2014.03.03

R: Algorithms using local modularity

localModularity

R Documentation

Algorithms using local modularity

Description

localModularity uses the local modularity to identify the local community structure around a certain vertex

localModularityWang uses the local modularity to identify the community structure of the entire network

Usage

localModularity(adjacency, srcV, k)
localModularityWang(adjacency,numRandom=0)

Arguments

`adjacency`	A nonnegative symmetric adjacency matrix of the network whose community structur will be analyzed
`srcV`	A given vertex whose local community structure should be determined by `localModularity`
`k`	The maximum number of vertices to add to the local community of `srcV`
`numRandom`	The number of random networks with which the modularity of the resulting community structure should be compared (default: no comparison). see details below for further explanation of the used null model.

Details

The used random networks have the same number of vertices and the same degree distribution as the original network.

Value

The result for localModularity is returned as a list with the following components

`local community structure`	Vertices assigned to the same community as the source vertex `srcV`
`local modularity`	The local modularity value for the determined local community

The result for localModularityWang is returned as a list with the following components

`number of communities`	The number of communities detected by the algorithm
`modularity`	The modularity of the detected community structure
`mean`	The mean of the modularity values for random networks, only computed if `numRandom>0`
`standard deviation`	The standard deviation of the modularity values for random networks, only computed if `numRandom>0`
`community structure`	The community structure of the examined network given by a vector assigning each vertex its community number
`random modularity values`	The list of the modularity values for random networks, only computed if `numRandom>0`

Author(s)

Maria Schelling, Cang Hui

References

Clauset, A. Finding local community structure in networks. Phys. Rev. E, 72:026132, Aug 2005.

Wang, X., Chen, G. and Lu, H. A very fast algorithm for detecting community structures in complex networks. Physica A: Statistical Mechanics and its Applications, 384(2):667-674, 2007.

Examples

#unweighted network
randomgraph1 <- erdos.renyi.game(10, 0.3, type="gnp",directed = FALSE, loops = FALSE)

#to ensure that the graph is connected
vertices1 <- which(clusters(randomgraph1)$membership==1)  
graph1 <- induced.subgraph(randomgraph1,vertices1)

adj1 <- get.adjacency(graph1)
result1 <- localModularity(adj1, srcV=1, k=4)

#weighted network
randomgraph2 <- erdos.renyi.game(10, 0.3, type="gnp",directed = FALSE, loops = FALSE)

#to ensure that the graph is connected
vertices2 <- which(clusters(randomgraph2)$membership==1)  
graph2 <- induced.subgraph(randomgraph2,vertices2)
graph2 <- set.edge.attribute(graph2, "weight", value=runif(ecount(graph2),0,1))

adj2 <- get.adjacency(graph2, attr="weight")
result2 <- localModularityWang(adj2)