wfg: Weighted Fast Greedy Algorithm

In wfg: 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='')

wfg documentation built on May 2, 2019, 9:43 a.m.