package edu.cmu.graphchi.apps;
import edu.cmu.graphchi.*;
import edu.cmu.graphchi.datablocks.FloatConverter;
import edu.cmu.graphchi.datablocks.FloatPair;
import edu.cmu.graphchi.datablocks.FloatPairConverter;
import edu.cmu.graphchi.engine.GraphChiEngine;
import edu.cmu.graphchi.engine.VertexInterval;
import edu.cmu.graphchi.io.CompressedIO;
import edu.cmu.graphchi.preprocessing.EdgeProcessor;
import edu.cmu.graphchi.preprocessing.FastSharder;
import edu.cmu.graphchi.preprocessing.VertexIdTranslate;
import edu.cmu.graphchi.preprocessing.VertexProcessor;
import edu.cmu.graphchi.util.IdFloat;
import edu.cmu.graphchi.util.Toplist;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.util.TreeSet;
import java.util.logging.Logger;
/**
* Weighted Pagerank.
* Contributed by Jerry Ye, 2014.
*/
public class WeightedPagerank implements GraphChiProgram<Float, FloatPair> {
private static Logger logger = ChiLogger.getLogger("weighted_pagerank");
public void update(ChiVertex<Float, FloatPair> vertex, GraphChiContext context) {
if (context.getIteration() == 0) {
/* Initialize on first iteration */
vertex.setValue(1.0f);
} else {
/* On other iterations, set my value to be the weighted
average of my in-coming neighbors pageranks.
*/
float sum = 0.f;
for(int i=0; i<vertex.numInEdges(); i++) {
sum += vertex.inEdge(i).getValue().second;
}
vertex.setValue(0.15f + 0.85f * sum);
}
/* Accumulate edge weights */
float edgeWeightSum = 0.f;
for(int i=0; i<vertex.numOutEdges(); i++) {
edgeWeightSum += vertex.outEdge(i).getValue().first;
}
/* Write my value (divided by my out-degree) to my out-edges so neighbors can read it. */
for(int i=0; i<vertex.numOutEdges(); i++) {
FloatPair curValue = vertex.outEdge(i).getValue();
float edgeWeight = vertex.outEdge(i).getValue().first;
vertex.outEdge(i).setValue(new FloatPair(curValue.first, vertex.getValue() * edgeWeight/edgeWeightSum));
}
}
/**
* Callbacks (not needed for Pagerank)
*/
public void beginIteration(GraphChiContext ctx) {}
public void endIteration(GraphChiContext ctx) {}
public void beginInterval(GraphChiContext ctx, VertexInterval interval) {}
public void endInterval(GraphChiContext ctx, VertexInterval interval) {}
public void beginSubInterval(GraphChiContext ctx, VertexInterval interval) {}
public void endSubInterval(GraphChiContext ctx, VertexInterval interval) {}
/**
* Initialize the sharder-program.
* @param graphName
* @param numShards
* @return
* @throws java.io.IOException
*/
protected static FastSharder createSharder(String graphName, int numShards) throws IOException {
return new FastSharder<Float, FloatPair>(graphName, numShards, new VertexProcessor<Float>() {
public Float receiveVertexValue(int vertexId, String token) {
return (token == null ? 0.f : Float.parseFloat(token));
}
}, new EdgeProcessor<FloatPair>() {
public FloatPair receiveEdge(int from, int to, String token) {
return new FloatPair(Float.parseFloat(token), 0.f);
}
}, new FloatConverter(), new FloatPairConverter());
}
/**
* Usage: java edu.cmu.graphchi.demo.PageRank graph-name num-shards filetype(edgelist|adjlist)
* For specifying the number of shards, 20-50 million edges/shard is often a good configuration.
*/
public static void main(String[] args) throws Exception {
String baseFilename = args[0];
int nShards = Integer.parseInt(args[1]);
String fileType = (args.length >= 3 ? args[2] : null);
CompressedIO.disableCompression();
/* Create shards */
FastSharder sharder = createSharder(baseFilename, nShards);
if (baseFilename.equals("pipein")) { // Allow piping graph in
sharder.shard(System.in, fileType);
} else {
if (!new File(ChiFilenames.getFilenameIntervals(baseFilename, nShards)).exists()) {
sharder.shard(new FileInputStream(new File(baseFilename)), fileType);
} else {
logger.info("Found shards -- no need to preprocess");
}
}
/* Run GraphChi */
GraphChiEngine<Float, FloatPair> engine = new GraphChiEngine<Float, FloatPair>(baseFilename, nShards);
engine.setEdataConverter(new FloatPairConverter());
engine.setVertexDataConverter(new FloatConverter());
engine.setModifiesInedges(false); // Important optimization
engine.run(new WeightedPagerank(), 4);
logger.info("Ready.");
/* Output results */
int i = 0;
VertexIdTranslate trans = engine.getVertexIdTranslate();
TreeSet<IdFloat> top20 = Toplist.topListFloat(baseFilename, engine.numVertices(), 20);
for(IdFloat vertexRank : top20) {
System.out.println(++i + ": " + trans.backward(vertexRank.getVertexId()) + " = " + vertexRank.getValue());
}
}
}