SequentialPageRank.java Source code

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Here is the source code for SequentialPageRank.java

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/*
 * Cloud9: A Hadoop toolkit for working with big data
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); you
 * may not use this file except in compliance with the License. You may
 * obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
 * implied. See the License for the specific language governing
 * permissions and limitations under the License.
 */

import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.Arrays;
import java.util.PriorityQueue;
import java.util.Set;

import org.apache.commons.cli.CommandLine;
import org.apache.commons.cli.CommandLineParser;
import org.apache.commons.cli.GnuParser;
import org.apache.commons.cli.HelpFormatter;
import org.apache.commons.cli.OptionBuilder;
import org.apache.commons.cli.Options;
import org.apache.commons.cli.ParseException;
import org.apache.hadoop.util.ToolRunner;

import edu.uci.ics.jung.algorithms.cluster.WeakComponentClusterer;
import edu.uci.ics.jung.algorithms.importance.Ranking;
import edu.uci.ics.jung.algorithms.scoring.PageRank;
import edu.uci.ics.jung.graph.DirectedSparseGraph;

/**
 * <p>
 * Program that computes PageRank for a graph using the <a
 * href="http://jung.sourceforge.net/">JUNG</a> package (2.0 alpha1). Program takes two command-line
 * arguments: the first is a file containing the graph data, and the second is the random jump
 * factor (a typical setting is 0.15).
 * </p>
 *
 * <p>
 * The graph should be represented as an adjacency list. Each line should have at least one token;
 * tokens should be tab delimited. The first token represents the unique id of the source node;
 * subsequent tokens represent its link targets (i.e., outlinks from the source node). For
 * completeness, there should be a line representing all nodes, even nodes without outlinks (those
 * lines will simply contain one token, the source node id).
 * </p>
 *
 * @author Jimmy Lin
 */
public class SequentialPageRank {
    private SequentialPageRank() {
    }

    private static final String INPUT = "input";
    private static final String JUMP = "jump";

    @SuppressWarnings({ "static-access" })
    public static void main(String[] args) throws IOException {
        Options options = new Options();

        options.addOption(OptionBuilder.withArgName("path").hasArg().withDescription("input path").create(INPUT));
        options.addOption(
                OptionBuilder.withArgName("val").hasArg().withDescription("random jump factor").create(JUMP));

        CommandLine cmdline = null;
        CommandLineParser parser = new GnuParser();

        try {
            cmdline = parser.parse(options, args);
        } catch (ParseException exp) {
            System.err.println("Error parsing command line: " + exp.getMessage());
            System.exit(-1);
        }

        if (!cmdline.hasOption(INPUT)) {
            System.out.println("args: " + Arrays.toString(args));
            HelpFormatter formatter = new HelpFormatter();
            formatter.setWidth(120);
            formatter.printHelp(SequentialPageRank.class.getName(), options);
            ToolRunner.printGenericCommandUsage(System.out);
            System.exit(-1);
        }

        String infile = cmdline.getOptionValue(INPUT);
        float alpha = cmdline.hasOption(JUMP) ? Float.parseFloat(cmdline.getOptionValue(JUMP)) : 0.15f;

        int edgeCnt = 0;
        DirectedSparseGraph<String, Integer> graph = new DirectedSparseGraph<String, Integer>();

        BufferedReader data = new BufferedReader(new InputStreamReader(new FileInputStream(infile)));

        String line;
        while ((line = data.readLine()) != null) {
            line.trim();
            String[] arr = line.split("\\t");

            for (int i = 1; i < arr.length; i++) {
                graph.addEdge(new Integer(edgeCnt++), arr[0], arr[i]);
            }
        }

        data.close();

        WeakComponentClusterer<String, Integer> clusterer = new WeakComponentClusterer<String, Integer>();

        Set<Set<String>> components = clusterer.transform(graph);
        int numComponents = components.size();
        System.out.println("Number of components: " + numComponents);
        System.out.println("Number of edges: " + graph.getEdgeCount());
        System.out.println("Number of nodes: " + graph.getVertexCount());
        System.out.println("Random jump factor: " + alpha);

        // Compute PageRank.
        PageRank<String, Integer> ranker = new PageRank<String, Integer>(graph, alpha);
        ranker.evaluate();

        // Use priority queue to sort vertices by PageRank values.
        PriorityQueue<Ranking<String>> q = new PriorityQueue<Ranking<String>>();
        int i = 0;
        for (String pmid : graph.getVertices()) {
            q.add(new Ranking<String>(i++, ranker.getVertexScore(pmid), pmid));
        }

        // Print PageRank values.
        System.out.println("\nPageRank of nodes, in descending order:");
        Ranking<String> r = null;
        while ((r = q.poll()) != null) {
            System.out.println(r.rankScore + "\t" + r.getRanked());
        }
    }
}