[Neo4j] Anyone try out the PageRank function on Gremlin?
Anyone try out the PageRank function on Gremlin? https://github.com/tinkerpop/gremlin/wiki/Working-with-JUNG-Algorithms/0506c193f30abe0bc18d40d7a08c9257d9311b13 How does it perform with just under 100k nodes on a sparse graph (3000 relationship max, average of 100)? I've been doing my pagerank via the power method in rb-gsl and while it's fine for around 10k items, it's sucking all the memory on my server when trying to do 92k items. -- View this message in context: http://neo4j-community-discussions.438527.n3.nabble.com/Anyone-try-out-the-PageRank-function-on-Gremlin-tp3560800p3560800.html Sent from the Neo4j Community Discussions mailing list archive at Nabble.com. ___ Neo4j mailing list User@lists.neo4j.org https://lists.neo4j.org/mailman/listinfo/user
Re: [Neo4j] Anyone try out the PageRank function on Gremlin?
Hi, Anyone try out the PageRank function on Gremlin? https://github.com/tinkerpop/gremlin/wiki/Working-with-JUNG-Algorithms/0506c193f30abe0bc18d40d7a08c9257d9311b13 How does it perform with just under 100k nodes on a sparse graph (3000 relationship max, average of 100)? I've been doing my pagerank via the power method in rb-gsl and while it's fine for around 10k items, it's sucking all the memory on my server when trying to do 92k items. Blueprints ( https://github.com/tinkerpop/blueprints/wiki/JUNG-Ouplementation ) implements the JUNG graph interface and thus, makes any Blueprints-enabled graph database into a JUNG graph. Unfortunately, JUNG was engineered from the perspective of in-memory use. As such, you will be running into memory issues on very large graphs. For example, if you have a 1million+ vertex graph and you are running PageRank on it, then your eigenvector vector is 1million+ entries. JUNG isn't serializing this vector to disk for you---its doing it all in memory. And if you don't have the memory to support a 1million+ vector (i.e. MapVertex,Double), then, well So, in short, be wary of doing memory intensive algorithms with JUNG (i.e. understand the intermediate data structures generated from the various supported graph algorithms). For non-memory intensive algorithms like shortest path, it should meet your needs. Into the future, TinkerPop will be filling out Furnace (http://furnace.tinkerpop.com) and this package will provide memory conscious implementations of classic and non-classical graph algorithms. HTH, Marko. http://markorodriguez.com ___ Neo4j mailing list User@lists.neo4j.org https://lists.neo4j.org/mailman/listinfo/user
Re: [Neo4j] Anyone try out the PageRank function on Gremlin?
Has anyone looked at the new? apache commons graph library? Michael Am 05.12.2011 um 16:38 schrieb Marko Rodriguez: Hi, Anyone try out the PageRank function on Gremlin? https://github.com/tinkerpop/gremlin/wiki/Working-with-JUNG-Algorithms/0506c193f30abe0bc18d40d7a08c9257d9311b13 How does it perform with just under 100k nodes on a sparse graph (3000 relationship max, average of 100)? I've been doing my pagerank via the power method in rb-gsl and while it's fine for around 10k items, it's sucking all the memory on my server when trying to do 92k items. Blueprints ( https://github.com/tinkerpop/blueprints/wiki/JUNG-Ouplementation ) implements the JUNG graph interface and thus, makes any Blueprints-enabled graph database into a JUNG graph. Unfortunately, JUNG was engineered from the perspective of in-memory use. As such, you will be running into memory issues on very large graphs. For example, if you have a 1million+ vertex graph and you are running PageRank on it, then your eigenvector vector is 1million+ entries. JUNG isn't serializing this vector to disk for you---its doing it all in memory. And if you don't have the memory to support a 1million+ vector (i.e. MapVertex,Double), then, well So, in short, be wary of doing memory intensive algorithms with JUNG (i.e. understand the intermediate data structures generated from the various supported graph algorithms). For non-memory intensive algorithms like shortest path, it should meet your needs. Into the future, TinkerPop will be filling out Furnace (http://furnace.tinkerpop.com) and this package will provide memory conscious implementations of classic and non-classical graph algorithms. HTH, 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
