Java tutorial
/** * This file is part of the Kompics P2P Framework. * * Copyright (C) 2009 Swedish Institute of Computer Science (SICS) * Copyright (C) 2009 Royal Institute of Technology (KTH) * * Kompics is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * as published by the Free Software Foundation; either version 2 * of the License, or (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. */ package se.sics.gvod.common; import se.sics.gvod.net.VodAddress; import java.util.HashMap; import java.util.LinkedList; import java.util.Queue; import java.util.TreeMap; import org.apache.commons.math.stat.descriptive.SummaryStatistics; /** * The <code>GraphUtil</code> class contains some utility code to generate an * adjacency matrix for a directed graph and use it to compute the diameter, * average path length, and the clustering coefficient, using n BFS traversals. * * You should extend this class to do the same thing for the undirected * graph corresponding to this directed graph, to obtain results for the * diameter and path length that are comparable to the results in the GVod * paper, if you are interested. * * @author Cosmin Arad <cosmin@sics.se> * @version $Id: GraphUtil.java 1118 2009-09-01 08:36:14Z Cosmin $ */ public class GraphUtil { int n; byte m[][]; public int dist[][]; double inDegree[]; int outDegree[]; double clustering[]; VodAddress[] a; HashMap<VodAddress, Integer> map; int[][] neighbors; SummaryStatistics inStats, outStats; int diameter = 0, infinitePathCount = 0; double avgPathLength = 0, avgIn = 0, avgOut = 0, avgClustering = 0, utilitySetNbChange = 0, upperSetNbChange = 0, nbCycles = 0; double minIn = n, maxIn = 0; public GraphUtil(TreeMap<VodAddress, VodNeighbors> alivePeers) { super(); n = alivePeers.size(); m = new byte[n][n]; dist = new int[n][n]; inDegree = new double[n]; outDegree = new int[n]; clustering = new double[n]; a = new VodAddress[n]; map = new HashMap<VodAddress, Integer>(); neighbors = new int[n][]; inStats = new SummaryStatistics(); outStats = new SummaryStatistics(); // map all alive nodes to a contiguous sequence of integers { int p = 0; for (VodAddress address : alivePeers.keySet()) { VodAddress src = (VodAddress) address; utilitySetNbChange += (alivePeers.get(src).getUtilitySetNbChange() / alivePeers.get(src).getNbCycles()); upperSetNbChange += (alivePeers.get(src).getUpperSetNbChange() / alivePeers.get(src).getNbCycles()); nbCycles += alivePeers.get(src).getNbCycles(); a[p] = src; map.put(src, p); p++; } } // build adjacency matrix int d = -1; { try { for (int s = 0; s < a.length; s++) { VodAddress src = a[s]; VodNeighbors neigh = alivePeers.get(src); int nn = 0; for (VodDescriptor desc : neigh.getRandomSetDescriptors()) { VodAddress dst = desc.getVodAddress(); if (!map.containsKey(dst)) { continue; } d = map.get(dst); m[s][d] = 1; inDegree[d]++; outDegree[s]++; nn++; } neighbors[s] = new int[nn]; nn = 0; for (VodDescriptor desc : neigh.getRandomSetDescriptors()) { VodAddress dst = desc.getVodAddress(); if (map.containsKey(dst)) { neighbors[s][nn++] = map.get(dst); } } } } catch (Exception e) { e.printStackTrace(); System.exit(1); } } // build distance matrix, clustering coefficient, average path length // diameter and average degrees { for (int i = 0; i < n; i++) { bfs(i, dist[i]); // we compute the clustering coefficient here int neigh[] = neighbors[i]; if (neigh.length <= 1) { clustering[i] = 1.0; continue; } int edges = 0; for (int j = 0; j < neigh.length; j++) { for (int k = j + 1; k < neigh.length; k++) { if (m[neigh[j]][neigh[k]] > 0 || m[neigh[k]][neigh[j]] > 0) { ++edges; } } } clustering[i] = ((edges * 2.0) / neigh.length) / (neigh.length - 1); } int k = 0; for (int i = 0; i < n; i++) { for (int j = 0; j < n; j++) { if (i == j) continue; if (dist[i][j] == n) { infinitePathCount++; continue; } if (dist[i][j] > diameter) { diameter = dist[i][j]; } avgPathLength = (avgPathLength * k + dist[i][j]) / (k + 1); k++; } inStats.addValue(inDegree[i]); outStats.addValue(outDegree[i]); // avgIn = (avgIn * i + inDegree[i]) / (i + 1); // minIn = minIn > inDegree[i] ? inDegree[i] : minIn; // maxIn = maxIn < inDegree[i] ? inDegree[i] : maxIn; // avgOut = (avgOut * i + outDegree[i]) / (i + 1); avgClustering = (avgClustering * i + clustering[i]) / (i + 1); } } } private void bfs(int v, int d[]) { Queue<Integer> q = new LinkedList<Integer>(); for (int i = 0; i < n; i++) { d[i] = n; // also means that the node has not been visited } d[v] = 0; q.offer(v); q.offer(0); // depth of v while (!q.isEmpty()) { int u = q.poll(); int du = q.poll(); // depth of u for (int t = 0; t < neighbors[u].length; t++) { if (d[neighbors[u][t]] == n) { // on the first encounter, add to the queue d[neighbors[u][t]] = du + 1; q.offer(neighbors[u][t]); q.offer(du + 1); } } } } public int getInDegree(int v) { if (v < n) { return (int) inDegree[v]; } else { return 0; } } public int getOutDegree(int v) { if (v < n) { return outDegree[v]; } else { return 0; } } public double getClustering(int v) { if (v < n) { return clustering[v]; } else { return 0; } } public double getMinInDegree() { // return minIn; return inStats.getMin(); } public double getMaxInDegree() { // return maxIn; return inStats.getMax(); } public double getMeanInDegree() { // return avgIn; return inStats.getMean(); } public double getInDegreeStdDev() { // return avgIn; return inStats.getStandardDeviation(); } public double getMeanOutDegree() { // return avgOut; return outStats.getMean(); } public double getMeanClusteringCoefficient() { return avgClustering; } public double getMeanPathLength() { return avgPathLength; } public int getDiameter() { return diameter; } public SummaryStatistics getInStats() { return inStats; } public SummaryStatistics getOutStats() { return outStats; } public int getNetworkSize() { return n; } public double[] getInDegrees() { return inDegree; } public int getNodeIndexByAddress(VodAddress address) { return map.get(address); } public int getInfinitePathCount() { return infinitePathCount; } public double getAvgUpperSetNbChange() { return upperSetNbChange / n; } public double getAvgUtilitySetNbChange() { return utilitySetNbChange / n; } }