Hi, That's true. I have developed the basic version with Igraph. Can you tell me about any other library that I can use to implement the algorithm for massive graphs
Thanks, Ruchika On 15 Dec 2016 18:12, "Tamas Nepusz" <[email protected]> wrote: > 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. > > _______________________________________________ > igraph-help mailing list > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help > >
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