I'm using: import org.neo4j.graphalgo.impl.centrality.EigenvectorCentrality; import org.neo4j.graphalgo.impl.centrality.EigenvectorCentralityArnoldi; import org.neo4j.graphalgo.impl.centrality.EigenvectorCentralityPower;
The variance I am seeing is far greater than anything that could be explained by floating point precision issues. For example, a result coming back after one call as 0.045 and then on the next call with identical options it could return 0.038. I glanced over the code and I see that they both use java.util.Random, so that could explain why it is not deterministic. Maybe that answers everything. Unfortunately, what it means is that you might randomly have two subsequent calls that appear to return similar results, but actually you have not zeroed in on the correct answer within the actual level of precision that is desired. The JavaDoc explicitly states that precision doesn't means proximity to correct result, but it doesn't make the results less unsatisfying. -Paul -----Original Message----- From: user-boun...@lists.neo4j.org [mailto:user-boun...@lists.neo4j.org] On Behalf Of Marko Rodriguez Sent: Tuesday, November 09, 2010 6:06 PM To: Neo4j user discussions Subject: Re: [Neo4j] Eigenvector Centrality subclasses Hey Paul, > I get inconsistent results from run to run using eigenvector centrality. It > doesn't seem to matter which implementation I use but I have used Arnoldi > most, for no reason other than it returns the iteration count. Given that eigenvector components sum to 1, and when dealing with large graphs, you may be running into floating point precision issues. In general, different eigenvector methods may have small variations in their values (even though its the same eigenvector!), but, if you are getting Spearman rank order correlation ~1.0, then I think its 'all good.' Also, note that for those eigenvector centrality implementations that are based on random walk, variations are sure to show up. > The iteration count is not consistent from run to run when run against the > exact same graph using the exact same precision. In a graph with 32 nodes > and 117 edges, I get anywhere from 18 to 24 iterations needed to get a > precision of 0.001. The variance is easier to see when the test is run on > different computers. Hmm... What code are you using? I'm talking in general and not specifically about anything Neo4j related... Thanks, Marko. http://markorodriguez.com _______________________________________________ Neo4j mailing list User@lists.neo4j.org https://lists.neo4j.org/mailman/listinfo/user _______________________________________________ Neo4j mailing list User@lists.neo4j.org https://lists.neo4j.org/mailman/listinfo/user
