| Type: | Package |
| Title: | Weighted Fast Greedy Algorithm |
| Version: | 0.1 |
| Date: | 2016-02-21 |
| Author: | Han Yu [aut, cre], Rachael Hageman Blair [aut] |
| Maintainer: | Han Yu <hyu9@buffalo.edu> |
| Depends: | R (≥ 3.1.0), igraph |
| Description: | Implementation of Weighted Fast Greedy algorithm for community detection in networks with mixed types of attributes. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Packaged: | 2016-02-24 18:37:35 UTC; Han |
| NeedsCompilation: | no |
| Repository: | CRAN |
| Date/Publication: | 2016-02-25 00:23:11 |
Simulation of Networks with Community Structures
Description
Simulation of networks under the framework by Girvan and Newman. The vertices are connected with each other randomly and independents with probability p.in (within same community) and p.out (between communities).
Usage
network.simu(nv = c(32, 32, 32, 32),
p.in = c(0.323, 0.323, 0.323, 0.323),
p.out = 0.0625, p.del = 0)
Arguments
nv |
a vector of community sizes. The number of communities equals the number of elements in this vector. |
p.in |
a vector of probability of a node to be randomly linked to other nodes in the same community. |
p.out |
the probability of a node to be randomly linked to nodes in other communities. |
p.del |
the proportion of links that are randomly deleted. |
Value
net |
The simulated network. |
group |
The membership of vertices. |
Author(s)
Han Yu & Rachael Hageman Blair
References
Girvan, Michelle, and Mark EJ Newman. "Community structure in social and biological networks." Proceedings of the national academy of sciences 99.12 (2002): 7821-7826.
Examples
## simulation of a network with four communities, each with size 32
library(wfg)
nv = c(32, 32, 32, 32)
p.in = c(0.452, 0.452, 0.452, 0.452)
p.out = 0.021
p.del = 0
net.simu <- network.simu(nv=nv, p.in=p.in, p.out=p.out, p.del=p.del)
net <- net.simu$net
group <- net.simu$group
## plot simulated network with vertices colored by membership
V(net)$size <- 7
V(net)$color <- group
plot(net, vertex.label='')
Weighted Fast Greedy Algorithm
Description
Implementation of weighted fast greedy algorithm for community detection in networks with mixed types of attributes.
Usage
wfg(net, attr=NULL, under.sample=FALSE, prioritize=FALSE)
Arguments
net |
network for community detection |
attr |
data frame of attribute information. The default value is NULL, when no attribute information will be used. Under default this method is identical to fast greedy community detection algorithm. |
under.sample |
a boolean parameter. When it is TRUE, the vertex pairs without links will be under-sampled to have the same number as that of the linked pairs of vertices. |
prioritize |
a boolean parameter. When it is TRUE, a matrix of cummunity-specific coefficients will be returned, by which the communities can be prioritized. |
Details
Each column of attr data frame can be a vector with type of either numeric (continuous) or factor (categorical). The matrix of cummunity-specific coefficients gives estimates as to the relative homogeneity of each attribute within each community. Specifically, a negative beta with large absolute value indicates corresponding attribute is homogeneous.
Value
beta |
Estimates of coefficients from logistic regression. |
beta.matrix |
Estimates of community specific coefficients. |
memb |
Community membership of vertices. |
Author(s)
Han Yu & Rachael Hageman Blair
References
Clauset, Aaron, Mark EJ Newman, and Cristopher Moore. "Finding community structure in very large networks." Physical review E 70.6 (2004): 066111.
Examples
##### implementation of wfg on a computer generated network with
##### structually relevant continuous attribute and irrelevant categorical attribute
set.seed(7)
##### set network properties
## four groups, each with 32 vertices
nv <- c(32,32,32,32)
l <- length(nv)
## obtain p.in and p.out from z.out
z.out <- 6
z.in <- 16-z.out
p.out <- z.out/96
p.in <- rep(z.in/31, l)
##### simulate network
net.simu <- network.simu(nv=nv, p.in=p.in, p.out=p.out, p.del=0)
net <- net.simu$net
group <- net.simu$group
##### simulate attributes
## separation of continuous attribute
delta <- 1
## p's for the multinomial distribution of categorical attributes
p1 <- 0.25
p2 <- (1-p1)/3
attr1 <- c(rnorm(nv[1],0), rnorm(nv[2],1*delta), rnorm(nv[3],2*delta), rnorm(nv[4],3*delta))
attr2 <- c(sample(c(1,2,3,4), size=nv[1], prob=c(p1, p2, p2, p2), replace=TRUE),
sample(c(1,2,3,4), size=nv[2], prob=c(p2, p1, p2, p2), replace=TRUE),
sample(c(1,2,3,4), size=nv[3], prob=c(p2, p2, p1, p2), replace=TRUE),
sample(c(1,2,3,4), size=nv[4], prob=c(p2, p2, p2, p1), replace=TRUE))
attributes <- data.frame(attr1, attr2)
##### implementation of wfg
wfg.result <- wfg(net=net, attr=attributes, under.sample = FALSE, prioritize = TRUE)
##### plot network colored by wfg result
V(net)$size <- 7
V(net)$color <- wfg.result$memb
plot(net, vertex.label='')