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},
}