# Finding communities in networks with R and igraph

Finding communities in networks is often a common task under the paradigm of complex systems. Doing it in R is very easy and there are several ways to do community partitioning of graphs using very different packages. The one I’m talking here is ipgrah.

igraph is a lovely library to work with graphs. 95% of what you’ll need is available in igraph with the advantage that the libraries are written in C and therefore are fast as hell.

Ok, now for the list of algorithms that you might be interested:

walktrap.community

This algorithm finds densely connected subgraphs by performing random walks. The idea is that random walks will tend to stay inside communities instead of jumping to other communities.

Pascal Pons, Matthieu Latapy: Computing communities in large networks using random walks, http://arxiv.org/abs/physics/0512106

edge.betweenness.community

This algorithm is the Girvan-Newman algorithm. Basically it is a divisive algorithm where at each step the edge with the highest betweenness is removed from the graph. For each division you can compute the modularity of the graph and then choose to cut the dendrogram where the process gives you the highest value of modularity.

M Newman and M Girvan: Finding and evaluating community structure in networks, Physical Review E 69, 026113 (2004)

fastgreedy.community

This algorithm is the Clauset-Newman-Moore algorithm. In this case the algorithm is agglomerative and at each step the merge is decided by the optimization of modularity that it produces as the result of the merge. This is very fast, but has the disadvantage of being a greedy algorithm, so it is might not produce the best overall community partitioning, although I find it very useful and very accurate.

A Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://www.arxiv.org/abs/cond-mat/0408187

spinglass.community

This algorithm uses as spin-glass model and simulated annealing to find the communities inside a network.

J. Reichardt and S. Bornholdt: Statistical Mechanics of Community Detection, Phys. Rev. E, 74, 016110 (2006), http://arxiv.org/abs/cond-mat/0603718

M. E. J. Newman and M. Girvan: Finding and evaluating community structure in networks, Phys. Rev. E 69, 026113 (2004)

An example:

```# First we load the ipgrah package library(igraph)   # let's generate two networks and merge them into one graph. g2 <- barabasi.game(50, p=2, directed=F) g1 <- watts.strogatz.game(1, size=100, nei=5, p=0.05) g <- graph.union(g1,g2)   # let's remove multi-edges and loops g <- simplify(g)   # let's see if we have communities here using the # Grivan-Newman algorithm # 1st we calculate the edge betweenness, merges, etc... ebc <- edge.betweenness.community(g, directed=F)   # Now we have the merges/splits and we need to calculate the modularity # for each merge for this we'll use a function that for each edge # removed will create a second graph, check for its membership and use # that membership to calculate the modularity mods <- sapply(0:ecount(g), function(i){ g2 <- delete.edges(g, ebc\$removed.edges[seq(length=i)]) cl <- clusters(g2)\$membership # March 13, 2014 - compute modularity on the original graph g # (Thank you to Augustin Luna for detecting this typo) and not on the induced one g2. modularity(g,cl) })   # we can now plot all modularities plot(mods, pch=20)   # Now, let's color the nodes according to their membership g2<-delete.edges(g, ebc\$removed.edges[seq(length=which.max(mods)-1)]) V(g)\$color=clusters(g2)\$membership   # Let's choose a layout for the graph g\$layout <- layout.fruchterman.reingold   # plot it plot(g, vertex.label=NA)   # if we wanted to use the fastgreedy.community agorithm we would do fc <- fastgreedy.community(g) com<-community.to.membership(g, fc\$merges, steps= which.max(fc\$modularity)-1) V(g)\$color <- com\$membership+1 g\$layout <- layout.fruchterman.reingold plot(g, vertex.label=NA)```

try it!

• Lee Sang Ho

I can not submit this example. Error happened in part of ‘mods’. My R’s version is 2.15.1. Please check and send corrected R-code to me. My E-mail add. is shlee@gwnu.ac.kr Have a good day…

• Hi, I just checked the code and it is working with me. Please check what version of igraph you have. The latest release of igraph (0.6.x) changed the way arrays are indexed starting from 1 instead of 0 in previous versions. This code used igraph 0.5.x so you might need to change the code accordingly.

• dogacozen

Hi I have another problem and I think it’s about \$removed.edges. Because when I changed it to \$removedEdges (which I think might be the igraph 0.6 version of the same piece of code), I got no errors but this time mods turns out to be all 0’s… I use igraph 0.6 by the way.

• New to igraph – using latest version. not sure how to change code re new method of indexing arrays

• Sanchari Sircar

Hi, I used the fastgreedy.community. How can I see the modules and the members in them ?

• If you do something like

c<-fastgreedy.community(g)

then

c\$membership

will give you the membership of your nodes in terms of the communities they belong.

Cheers,

• I’ve updated the finding communities with igraph example to correct for a bug.