JungGraphMeasures - PageRank and HITS implementations for large RDF graphs
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README.md

JungGraphMeasures

JungGraphMeasures - PageRank and HITS implementations for large RDF graphs

This projects uses JUNG — the Java Universal Network/Graph Framework to compute PageRank and HITS scores. Jung is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network.

##Settings

Parameters used while computing pagerank

PageRank damping factor: 0.85 //The probability at any step, that the person will continue
PageRank no of iterations: 100 //Number of iterations used before terminating
PageRank Tolerance: 0 //Minimum change from one step to the next
Alpha: 0.15 //Random jump probability, the probability of taking a random jump to an arbitrary vertex

Parameters used while computing HITS

No of iterations: 100 //Number of iterations used before terminating
Tolerance: 0 //Minimum change from one step to the next
Alpha: 0.15 //the probability of a hub giving some authority to all vertices, and of an authority increasing the score of   all hubs (not just those connected via links)

##Usage

PageRank and HITS are invoked by

<<inputTurtleFilePath>> <<PageRank or HITS>>

##Datasets

You can download the resulting datasets here DBpedia Pagerank and DBpedia HITS

##Citation

If you are using this dataset please cite as:

{dbpedia-graphmeasures,
  Author = {Dinesh Reddy},
  Title = {DBpedia GraphMeasures},
  Location = {http://semanticmultimedia.org/node/6},
  Resource type = {dataset},
  Publisher = {Hasso Plattner Institute},
  Publication date = {July 2014},
}