[igraph] automating computations on multiple graphs

[Danielle Li]

Hi,

I'm trying to do some basic social network analysis with igraph in R, but I'm new to R and haven't been able to find documentation on a couple basic things:

I want to run igraph's community detection algorithms on a couple thousand small graphs but don't know how to automate igraph looking at multiple graphs described in a single csv file.  My data look like something in ncol format, but with an additional column that has an ID for which graph the edge belongs in:

Graph ID | Vertex1 | Vertex2 | weight

1 | Alice | Bob | 2

1 | Alice | Chris | 1

1 | Alice | Jane | 2

1 | Bob | Jane | 2

1 | Chris | Jane | 3

2 | Alice | Tom | 2

2 | Alice | Kate | 1

2 | Kate | Tom | 3

2 | Tom | Mike | 2

so on and so forth for about 2000 graph IDs, each with about 20-40 vertices.

I want to run community detection on each graph separately--to look only at the edges when the graph identifier is 1, make calculations on that graph, then do it again for 2 and so forth.  I suspect that this isn't related to igraph specifically--I just don't know the equivalent command in R for what in pseudo Stata code would read as:

forvalues i of 1/N {

temp_graph=subrows of the main csv file for which graphid==`i'

cs`i' = leading.eigenvector.community.step(temp_graph)

convert cs`i'$membership into a column in the original csv

}

I want the output to look something like:

Graph ID | Vertex1 | Vertex2 | weight | Vertex 1 membership | Vertex 2 membership | # of communities in the graph

1 | Alice | Bob | 2 | A | B | 2

1 | Alice | Chris | 1 | A | B | 2

1 | Alice | Jane | 2 | A | B | 2

1 | Bob | Jane | 2 | B | B | 2

1 | Chris | Jane | 3 | B | B | 2

2 | Alice | Tom | 2 | A | B | 3

2 | Alice | Kate | 1 | A | C | 3

2 | Kate | Tom | 3 |  C | B | 3

2 | Tom | Mike | 2 | B | C | 3

Here, the graphs are treated completely separately so that community A in graph 1 need not have anything to do with community A in graph 2. 

I would really appreciate any ideas you guys have.

Thank you!

Danielle