**sieste » R**, and kindly contributed to R-bloggers)

Network science is potentially useful for certain problems in data analysis, and I know close to nothing about it.

In this short post I present my first attempt at network analysis: A minimal example to construct and visualize an artificial undirected network with community structure in R. No network libraries are loaded. Only basic R-functions are used.

The final product of this post is this plot:

I am going to walk you through the code to produce the above plots. The complete code is given at the bottom of the post.

**The starting point**

First of all the network specification:

#network specs: K communities, N nodes, n[1] of them have #label 1, etc..., p(link inside)=p.in, p(link outside)=p.out K <- 3 N <- 40 n <- c(rep(floor(N/K),K-1),floor(N/K)+N%%K) labls <- rep(seq(K),n) p.in <- .9 p.out <- .1 pairs <- expand.grid(seq(N),seq(N)) uniq.pairs <- pairs[which(pairs[,1]<pairs[,2]),] uniq.pairs <-lapply(apply(uniq.pairs,1,list),unlist) # I hate R for this line

There are `N`

nodes and `K`

communities. `n[1]`

nodes are in community 1, `n[2]`

in community 2, etc.

The probability `p.in`

is the link probability between two nodes that are in the same community and `p.out`

is the link probability across communities.

The variable `labls`

contains the label of each node.

The variable `pairs`

contains all possible tuples of the set (1,…,N). `uniq.pairs`

is a list of 2-element vectors, containing only the unique links between nodes.

**The p-matrix**

Such a network with community structure is typically modeled by what I would call a p-matrix (the technical term is “stochastic block matrix”). The entry in the i-th row and j-th column tells me the probability that node i is connected to node j. Since the network is undirected, a link between i and j is equivalent to a link between j and i, hence the p-matrix is symmetric. Furthermore no loops are allowed here, so the diagonal elements of the p-matrix are zero.

Community structure leads to the p-matrix being block-diagonal with large constant entries `p.in`

in the blocks across the diagonal and small or vanishing entries `p.out`

outside of these blocks.

#plot the block matrix plot(NULL,xlim=c(1,N),ylim=c(1,N),asp=1,axes=F,xlab=NA,ylab=NA, main="p-matrix") lapply( uniq.pairs, function(z) { colr <- ifelse(labls[z[1]]==labls[z[2]],gray(1-p.in), gray(1-p.out)) points(z,N-z,pch=15,col=colr) } )

**The adjacency list**

Based on the network specifications, an adjacency list is constructed. The adjacency list is the list of links in the network. A link between nodes i and j is realized with probability `p.in`

if the nodes are in the same community, and with probability `p.out`

otherwise.

#sample an adjacency list adj.list <- lapply(uniq.pairs,function(z) { p <- ifelse( labls[z[1]]==labls[z[2]],p.in,p.out) ifelse(runif(1)<p,1,0)*z}) adj.list <- adj.list[ which(lapply(adj.list,sum)>0) ]

One way of visualizing the network is to plot the adjacency matrix which has dimension N*N and has a one at index (i,j) if nodes i and j are connected and a zero otherwise:

#plot the adjacency matrix plot(NULL,xlim=c(1,N),ylim=c(1,N),asp=1,axes=F,xlab=NA,ylab=NA, main="adjacency matrix") lapply( adj.list, function(z) { points(z,N-z,pch=15) } )

**Visualizing the links**

Another way of visualizing the network is to plot the individual nodes and connect them with lines according to their links:

#plot the network plot(NULL,xlim=c(0,K),ylim=c(0,1),axes=F,xlab=NA,ylab=NA, main="network") coords <- matrix(runif(2*N),ncol=2)+cbind(labls-1,rep(0,N)) lapply( adj.list, function(z) { x <- coords[ z,1 ] y <- coords[ z,2 ] lines(x,y,col="#55555544") } ) colrs <- rainbow(K) points( coords, pch=15, col=colrs[ labls ])

**So far so good**

This is it. As I said, it was the first step.

Next I want to look at ways to guess the node labels (the community structure) only based on the network structure, so stay tuned.

Thanks for watching.

———————-

The complete code:

###################################################### # construct and plot a stochastic block model, and a # corresponding network with community structure ###################################################### ################################################################ #network specs: K communities, N nodes, n[1] of them have #label 1, etc..., p(link inside)=p.in, p(link outside)=p.out ################################################################ N <- 40 K <- 3 n <- c(rep(floor(N/K),K-1),floor(N/K)+N%%K) labls <- rep(seq(K),n) p.in <- .9 p.out <- .1 ############################################################## #sample an adjacency list # pairs (uniq.pairs) ... list of possible (unique) node pairs # adj.list ... list of links between nodes ############################################################## pairs <- expand.grid(seq(N),seq(N)) uniq.pairs <- pairs[which(pairs[,1]<pairs[,2]),] uniq.pairs <-lapply(apply(uniq.pairs,1,list),unlist) # I hate R for this line adj.list <- lapply(uniq.pairs,function(z) { p <- ifelse( labls[z[1]]==labls[z[2]], p.in,p.out) ifelse(runif(1)<p,1,0)*z}) adj.list <- adj.list[ which(lapply(adj.list,sum)>0) ] #everything in one plot jpeg("network.jpg",width=500,height=500,quality=100) par(mar=c(2,2,4,2),cex.main=1.5) layout(t(matrix(c(1,2,3,3),nrow=2))) #plot the block matrix plot(NULL,xlim=c(1,N),ylim=c(1,N),asp=1,axes=F,xlab=NA,ylab=NA, main="p-matrix") lapply( uniq.pairs, function(z) { colr <- ifelse(labls[z[1]]==labls[z[2]],gray(1-p.in), gray(1-p.out)) points(z,N-z,pch=15,col=colr) } ) #plot the adjacency matrix plot(NULL,xlim=c(1,N),ylim=c(1,N),asp=1,axes=F,xlab=NA,ylab=NA, main="adjacency matrix") lapply( adj.list, function(z) { points(z,N-z,pch=15) } ) #plot the network plot(NULL,xlim=c(0,K),ylim=c(0,1),axes=F,xlab=NA,ylab=NA, main="network") coords <- matrix(runif(2*N),ncol=2)+cbind(labls-1, rep(0,N)) lapply( adj.list, function(z) { x <- coords[ z,1 ] y <- coords[ z,2 ] lines(x,y,col="#55555544") } ) colrs <- rainbow(K) points( coords, pch=15, col=colrs[ labls ]) dev.off()

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