Re: [igraph] plotting large graph

[Tamás Nepusz]

> thanks,
> the question should be plotting graph of thousand or hundred thousands of 
> nodes

The short answer is probably: don’t. Chances are that you end up with an 
untangible mess of nodes and edges drawn on top of each other, no matter how 
hard you try. Suppose that you have, say, 30.000 nodes, and you want to draw 
the network on a screen consisting of 1600x1200 pixels. That means that you 
have 64 pixels for each node, assuming that you fill the screen entirely, and 
you haven’t even drawn the edges or the labels of the nodes.

The long answer is: you need two things, 1) to calculate a layout that tells 
igraph where to put each node on the screen and 2) to actually draw the graph. 
Step 1 is probably easier but you basically have only one option for graphs of 
this size; use the DrL layout algorithm that is built into igraph, cross your 
fingers and hope that it comes up with something useful. (You may need to tweak 
its parameters, and you may have to experiment a lot). Step 2 should be simply 
a matter of passing the graph and the layout to the plot() function, but 
chances are that none of the native drawing backends are able to cope with the 
amount of graphics you have to draw on the screen. I managed to plot a graph 
with ~75K nodes once using igraph, and I used the DrL layout for that, but I 
had to implement the actual drawing “manually” instead of relying on the 
built-in plot functions because the built-in functions spend a lot of time 
doing things that I did not need and that only slowed things down (e.g., 
collecting relevant vertex and edge attributes that define the visual 
appearance of the nodes, drawing labels, arrowheads, calculating edge 
curvatures etc). Even with a custom drawing routine, drawing that graph at a 
reasonable size (20.000 x 15.000 pixels) took quite a few minutes.

> and determine node color depending on weight?
If you use the default plotting backend, it should only be a matter of 
assigning values to the “color” vertex attribute based on the weights before 
plotting.

T.