Re: [igraph] how to create a graph from an adjacency - like matrix

[Tony Larson]

Thanks Tamas,
I setted on the following code for best efficiency, which avoids the 
pitfalls of R slow-downs inherent when using for loops or apply():

df <- data.frame(peak = paste("n", 
row(libfactor.mat)[!is.na(libfactor.mat)], sep = ""), lib = paste("l", 
libfactor.mat[!is.na(libfactor.mat)], sep = ""))
g <- graph.data.frame(df)
V(g)$type <- substr(V(g)$name, 1, 4) == "l"
peak.proj <- bipartite.projection(g)[[1]]
peak.members <- clusters(peak.proj)$membership

This all executes in a couple of seconds with my 40523 * 10 matrix!

cheers
Tony








On 04/03/2013 22:14, Tamás Nepusz wrote:
> > I have a large numeric matrix in R as follows:
> >
> > > dim(libfactor.mat)
> > [1] 40523    10
> > > head(libfactor.mat)
> >       [,1]  [,2]  [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
> > [1,] 20884 19184 19185   NA   NA   NA   NA   NA   NA    NA
> > [2,] 18330 13979 13978   NA   NA   NA   NA   NA   NA    NA
> > [3,]   176 17101 21455   NA   NA   NA   NA   NA   NA    NA
> > [4,] 15166 15165 15166   NA   NA   NA   NA   NA   NA    NA
> > [5,] 14190 14306  7450   NA   NA   NA   NA   NA   NA    NA
> > [6,]  9196  1513  4054   NA   NA   NA   NA   NA   NA    NA
> > [...]
> > What I want to do is build a graph from this and decompose it so all compounds 
> > sharing any hits form unique clusters.
> I think the easiest way to do it is to build a compound-vs-libraryID bipartite 
> graph where the nodes are either compounds or library IDs and a compound node 
> is connected with its corresponding library ID nodes. Then you can run 
> bipartite.projection to project it into the compound graph where two compounds 
> will be connected if they share at least a single library ID. Unfortunately 
> there is no function in igraph to create a bipartite network from the 
> representation you have. I have tried to come up with a solution and it seems 
> pretty convoluted for me, but I'm no expert in R so maybe there's a better or 
> more elegant solution. Basically, we first construct an adjacency list using 
> apply():
>
> adj.list <- apply(libfactor.mat, function(x) paste("l", x[!is.na(x)], sep=""))
>
> Now we turn the adjacency list into an edge list (with vertex names):
>
> edge.list <- lapply(1:length(adj.list), function(x) as.vector(rbind(paste("n", x, 
> sep=""), adj.list[[x]])))
> edge.list <- Reduce(c, edge.list)
>
> Then into a data frame:
>
> df <- data.frame(matrix(edge.list, ncol=2, byrow=T))
>
> Then we can create our graph from the data frame:
>
> g <- graph.data.frame(df)
> V(g)$type <- substr(V(g)$name, 1, 1) == "l"
> projections <- bipartite.projection(g)
>
> projections[[1]] will then be the compound matrix you need.