> > I am following this research paper whose findings I have to replicate. And > one of their graphs has 5million nodes and 69 million edges. That's the > smallest dataset they are using. > igraph has no problems with a graph of that size on a decent machine. (Mine has 8 GB of RAM and an Erdos-Renyi random graph of that size fits easily). Larger graphs can become problematic -- but anyway, working with in-memory graphs and on-disk graphs is radically different, and igraph was designed for the former use-case, so it won't be of any help to you if your graph does not fit into RAM. The problem is that igraph makes assumptions about the cost of certain operations; for instance, it assumes that looking up the neighbors of a vertex can be done in constant time. These assumptions do not hold if the graph is on the disk because the operations get much more costly. So, in that case, you are better off either using another library that stores the graph in a database, or implement your algorithm from scratch.
T.
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