Hi! Building Neo4j components is quite easy in most cases, just make sure you have Maven installed. Instructions in the wiki: http://wiki.neo4j.org/content/Java_Setup_HowTo#Building_from_source
/anders On 11/10/2010 03:45 PM, Paul A. Jackson wrote: > I will look into adding a "deterministic" property that defaults to false for > backward compatibility and test to see that the deterministic results are > reasonable. > > I haven't built neo4j before so I can't commit to the success of this attempt. > > -Paul > > > > -----Original Message----- > From: neubauer.pe...@gmail.com [mailto:neubauer.pe...@gmail.com] On Behalf Of > Peter Neubauer > Sent: Wednesday, November 10, 2010 3:11 AM > To: Neo4j user discussions > Cc: Paul A. Jackson > Subject: Re: [Neo4j] Eigenvector Centrality subclasses > > Paul, Marko, > could you do a test on if the new Random(0) would be a good change? I > am not really into that algo, so I think you could do a much better > job there, given your expertise! > > Cheers, > > /peter neubauer > > GTalk: neubauer.peter > Skype peter.neubauer > Phone +46 704 106975 > LinkedIn http://www.linkedin.com/in/neubauer > Twitter http://twitter.com/peterneubauer > > http://www.neo4j.org - Your high performance graph database. > http://www.thoughtmade.com - Scandinavia's coolest Bring-a-Thing party. > > > > On Wed, Nov 10, 2010 at 12:19 AM, Paul A. Jackson<paul.jack...@pb.com> wrote: >> Perhaps if "new Random( System.currentTimeMillis() )" we replaced with "new >> Random( 0 )", you would get the benefits of pseudo random behavior but also >> deterministic results from run to run. >> >> -Paul >> >> -----Original Message----- >> From: Paul A. Jackson >> Sent: Tuesday, November 09, 2010 6:16 PM >> To: 'Neo4j user discussions' >> Subject: RE: [Neo4j] Eigenvector Centrality subclasses >> >> 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 >> > > _______________________________________________ > 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
