Good point Bernie. Ruchika, I suggest you taking a look at the influenceR R package https://github.com/rcc-uchicago/influenceR. The package uses both igraph and SANP, although not at the lower level. Either way, I think it might help you a bit.
Best, George G. Vega Yon +1 (626) 381 8171 http://cana.usc.edu/vegayon On Mon, Dec 19, 2016 at 9:28 AM, Bernie Hogan <[email protected]> wrote: > No! > > [Hi everyone, been ages,] > > neo4j is a stellar product that we use as a backbone to our program > NetworkCanvas, but it is woefully inefficient at the scale you're > describing. It's basically a database of networks as nodes and edges where > edges are a series of bi-directional hashes relating nodes for easy > traversal. The cypher language for querying it is neat. I wouldn't say > elegant but neat. Neo4j works very nicely with high dimensional data but it > is not the most efficient graph database. I might be wrong, but I was given > some serious grief lately for using neo4j by some CS people here at Oxford. > I told them that for me usability was more important than performance as my > current networks are extremely high dimension (multiplex, longitudinal, > many atributes) but not very large. They agreed but said I should look a > little further afield if I'm working towards more big big data networks. > > In the case mentioned here, you might want to have a look at Zen but it > doesn't seem very active: http://zen.networkdynamics.org > > Also, Jure Leskovec's SNAP is also geared towards very large networks and > can definitely handle the sort you're referring to > https://snap.stanford.edu/data/ It's been used to analyze hundreds of > millions of accounts and billions of edges on MSN among other things. I > haven't used either of these packages in ages though so YMMV. > > Take care, > Bernie > > Bernie Hogan, PhD > Research Fellow, Oxford Internet Institute > Faculty Fellow, Alan Turing Institute > University of Oxford > http://www.oii.ox.ac.uk/people/hogan > > > > On Mon, Dec 19, 2016 at 3:29 PM Ruchika Salwan <[email protected]> > wrote: > >> Hey, >> >> Thanks a lot Tamas !! will check it out for sure. You have been a great a >> help. :) >> >> Best, >> Ruchika >> >> On Mon, Dec 19, 2016 at 6:41 PM, Tamas Nepusz <[email protected]> >> wrote: >> >> I've heard that Neo4J is the de facto standard tool for dealing with >> graph databases. Never used it though. >> >> T. >> >> On Mon, Dec 19, 2016 at 12:32 PM, Ruchika Salwan < >> [email protected]> wrote: >> >> 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 >> >> >> _______________________________________________ >> igraph-help mailing list >> [email protected] >> https://lists.nongnu.org/mailman/listinfo/igraph-help >> >> >> >> _______________________________________________ >> igraph-help mailing list >> [email protected] >> https://lists.nongnu.org/mailman/listinfo/igraph-help >> >> >> _______________________________________________ >> igraph-help mailing list >> [email protected] >> https://lists.nongnu.org/mailman/listinfo/igraph-help >> > > _______________________________________________ > igraph-help mailing list > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help > >
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