| Title: | Weighted Fast Greedy Algorithm |
|---|---|
| Description: | Implementation of Weighted Fast Greedy algorithm for community detection in networks with mixed types of attributes. |
| Authors: | Han Yu [aut, cre], Rachael Hageman Blair [aut] |
| Maintainer: | Han Yu <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1 |
| Built: | 2024-11-14 02:58:00 UTC |
| Source: | https://github.com/cran/wfg |
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).
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)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)
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. |
net |
The simulated network. |
group |
The membership of vertices. |
Han Yu & Rachael Hageman Blair
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.
## 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='')## 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='')
Implementation of weighted fast greedy algorithm for community detection in networks with mixed types of attributes.
wfg(net, attr=NULL, under.sample=FALSE, prioritize=FALSE)wfg(net, attr=NULL, under.sample=FALSE, prioritize=FALSE)
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. |
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.
beta |
Estimates of coefficients from logistic regression. |
beta.matrix |
Estimates of community specific coefficients. |
memb |
Community membership of vertices. |
Han Yu & Rachael Hageman Blair
Clauset, Aaron, Mark EJ Newman, and Cristopher Moore. "Finding community structure in very large networks." Physical review E 70.6 (2004): 066111.
##### 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$memb plot(net, vertex.label='')##### implementation of wfg on a computer generated network with ##### structually relevant continuous attribute and irrelevant categorical attribute set.seed(7) ##### set network properties ## four groups, each with 32 vertices nv <- c(32,32,32,32) l <- length(nv) ## obtain p.in and p.out from z.out z.out <- 6 z.in <- 16-z.out p.out <- z.out/96 p.in <- rep(z.in/31, l) ##### simulate network net.simu <- network.simu(nv=nv, p.in=p.in, p.out=p.out, p.del=0) net <- net.simu$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='')