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https://issues.apache.org/jira/browse/TINKERPOP-2376?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17159237#comment-17159237
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ASF GitHub Bot commented on TINKERPOP-2376:
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spmallette merged pull request #1301:
URL: https://github.com/apache/tinkerpop/pull/1301
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> Probability distribution controlled by weight when using sample step
> --------------------------------------------------------------------
>
> Key: TINKERPOP-2376
> URL: https://issues.apache.org/jira/browse/TINKERPOP-2376
> Project: TinkerPop
> Issue Type: Improvement
> Components: process
> Affects Versions: 3.4.6
> Environment: Gremlin-Tinkerpop 3.4.6 on Fedora 32
> Reporter: zjxian
> Assignee: Stephen Mallette
> Priority: Minor
> Attachments: SampleGlobalStep.java, out.csv
>
>
> create a simple graph with 1 central node and 3 surronding nodes
> add 3 edges with equal weight (1) and form a stargraph
> traverse from center ( v[0] ) to other (3) nodes, sample(1) and record the
> destination node
> do that 10000 times
> estimated probabitlity distribution:
> v[1]:v[2]:v[3] = 3333:3333:3333 (1:1:1)
> what i got:
> v[1]:v[2]:v[3] = 3320:4439:2241
> I've checked some source file, like
> ([https://github.com/apache/tinkerpop/blob/master/gremlin-core/src/main/java/org/apache/tinkerpop/gremlin/process/traversal/step/filter/SampleGlobalStep.java]).
> The probability distribution shoud be like 1/3:4/9:2/9, which is very close
> to the results I got.
> I think some improvements is needed here to make "random walk" in tinkerpop
> really useful.
> the script i use:
> {code:java}
> //代码占位符
> conf = new BaseConfiguration()
> conf.setProperty("gremlin.tinkergraph.vertexIdManager","LONG")
> conf.setProperty("gremlin.tinkergraph.edgeIdManager","LONG")
> conf.setProperty("gremlin.tinkergraph.vertexPropertyIdManager","LONG");
> graph = TinkerGraph.open(conf)g=graph.traversal()
> for(i=0;i<=3;i++){
> g.addV().iterate()
> }
> for(i=1;i<=3;i++){
> g.V(0).addE("connect").property("weight",1).to(g.V(i)).iterate()
> }
> ["bash", "-c", "rm -f out.csv"].execute().waitFor()file=new
> File("out.csv")file.append("id\r\n")
> for(i=0;i<10000;i++){
> g.V(0).outE().sample(1).by("weight").otherV().map{file.append
> it.get().id()+"\r\n"}.iterate()
> }
> {code}
> see result in attached out.csv
>
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