62 lines
1.8 KiB
Plaintext
62 lines
1.8 KiB
Plaintext
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delete database pagerank;
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create database pagerank;
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open database pagerank;
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#
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# Dampening factor
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#
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let D = 0.85;
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#
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# Extended worker relation
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#
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restore Workers from TwoTwoWorkers;
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let WorkerNumber = Workers count;
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#
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# Partition function
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#
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let PartitionFunction = fun (id: int) (hashvalue(id, WorkerNumber));
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#
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# Edges of the Graph
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#
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restore Links from LinksStanfordNoDangling;
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#
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# Remove dangling Links => Might take a while on big graphs, you may want to check to long an iteration takes first.
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# You can also see the rate at which it's converging pretty quickly.
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# The Stanford dataset with ~2.3 million edges took 50-ish iterations of 1:30min each to complete
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#
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let RemoveDanglingLinks = fun (links: rel(tuple([Source: int, Target: int]))) (links feed sortby[Target] links feed project[Target] sort rdup links feed projectextend[;Target: .Source] sort rdup kmergediff kmergediff);
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#let NumberLinks = 100000000;
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#while Links count < NumberLinks do { update NumberLinks := Links count | update Links := RemoveDanglingLinks(Links) consume | query Links count } endwhile;
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#delete NumberLinks;
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#
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# Vertices of the Graph
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#
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let PagesPersistent = Links feed projectextend[;Id: .Source, Value: 0.0] sort rdup consume;
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let N = PagesPersistent count;
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let Minimum = ((1 - D) / N);
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update PagesPersistent := PagesPersistent feed projectextend[Id; Value: 1.0 / N] consume;
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let Pages = PagesPersistent feed mconsume;
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#
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# Connect Edge Pages => Leads to WAY! more Links, so use with caution
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#
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let ConnectEdgePages = fun (pages: rel(tuple([Id: int, Value: real])), links: rel(tuple([Source: int, Target: int]))) (links feed project[Target] sort links feed projectextend[;Target: .Source] sort mergediff projectextend[; Source: .Target] loopjoin[pages feed projectextend[; Target: .Id]]);
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# update Links := Links feed ConnectEdgePages(PagesPersistent, Links) concat sortby[Source] consume;
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