Java tutorial
/* Copyright 2008-2011 Gephi Authors : Patick J. McSweeney <pjmcswee@syr.edu>, Sebastien Heymann <seb@gephi.org> Website : http://www.gephi.org This file is part of Gephi. DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS HEADER. Copyright 2011 Gephi Consortium. All rights reserved. The contents of this file are subject to the terms of either the GNU General Public License Version 3 only ("GPL") or the Common Development and Distribution License("CDDL") (collectively, the "License"). You may not use this file except in compliance with the License. You can obtain a copy of the License at http://gephi.org/about/legal/license-notice/ or /cddl-1.0.txt and /gpl-3.0.txt. See the License for the specific language governing permissions and limitations under the License. When distributing the software, include this License Header Notice in each file and include the License files at /cddl-1.0.txt and /gpl-3.0.txt. 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Contributor(s): Portions Copyrighted 2011 Gephi Consortium. */ package org.gephi.statistics.plugin; import java.text.DecimalFormat; import java.text.NumberFormat; import java.util.Arrays; import java.util.Comparator; import java.util.HashMap; import java.util.Map; import org.gephi.statistics.spi.Statistics; import org.gephi.graph.api.Node; import org.gephi.data.attributes.api.AttributeTable; import org.gephi.data.attributes.api.AttributeColumn; import org.gephi.data.attributes.api.AttributeModel; import org.gephi.data.attributes.api.AttributeOrigin; import org.gephi.data.attributes.api.AttributeRow; import org.gephi.data.attributes.api.AttributeType; import org.gephi.graph.api.Edge; import org.gephi.graph.api.GraphController; import org.gephi.graph.api.GraphModel; import org.gephi.graph.api.HierarchicalDirectedGraph; import org.gephi.graph.api.HierarchicalGraph; import org.gephi.utils.longtask.spi.LongTask; import org.gephi.utils.progress.Progress; import org.gephi.utils.progress.ProgressTicket; import org.jfree.chart.ChartFactory; import org.jfree.chart.JFreeChart; import org.jfree.chart.plot.PlotOrientation; import org.jfree.data.xy.XYSeries; import org.jfree.data.xy.XYSeriesCollection; import org.openide.util.Lookup; import org.gephi.graph.api.NodeIterable; /** * Ref: Matthieu Latapy, Main-memory Triangle Computations for Very Large (Sparse (Power-Law)) Graphs, * in Theoretical Computer Science (TCS) 407 (1-3), pages 458-473, 2008 * * @author pjmcswee */ class Renumbering implements Comparator<EdgeWrapper> { public int compare(EdgeWrapper o1, EdgeWrapper o2) { if (o1.wrapper.getID() < o2.wrapper.getID()) { return -1; } else if (o1.wrapper.getID() > o2.wrapper.getID()) { return 1; } else { return 0; } } } /** * * @author pjmcswee */ class EdgeWrapper { public int count; public ArrayWrapper wrapper; public EdgeWrapper(int count, ArrayWrapper wrapper) { this.count = count; this.wrapper = wrapper; } } /** * * @author pjmcswee */ class ArrayWrapper implements Comparable { private EdgeWrapper[] array; private int ID; public Node node; /** Empty Constructor/ */ ArrayWrapper() { } /** * * @return The ID of this array wrapper */ public int getID() { return ID; } /** * * @return The adjacency array */ public EdgeWrapper[] getArray() { return array; } public void setArray(EdgeWrapper[] array) { this.array = array; } /** * * @param pArray */ ArrayWrapper(int ID, EdgeWrapper[] array) { this.array = array; this.ID = ID; } public void setID(int ID) { this.ID = ID; } /** * * @param pIndex * @return */ public int get(int index) { if (index >= array.length) { return -1; } return array[index].wrapper.ID; } public int getCount(int index) { if (index >= array.length) { return -1; } return array[index].count; } /** * * @return */ public int length() { return array.length; } /** * * @param o * @return */ public int compareTo(Object o) { ArrayWrapper aw = (ArrayWrapper) o; if (aw.length() < length()) { return -1; } if (aw.length() > length()) { return 1; } return 0; } } /** * * @author Patrick J. McSweeney */ public class ClusteringCoefficient implements Statistics, LongTask { public static final String CLUSTERING_COEFF = "clustering"; /** The avergage Clustering Coefficient.*/ private double avgClusteringCoeff; /**Indicates should treat graph as undirected.*/ private boolean isDirected; /** Indicates statistics should stop processing/*/ private boolean isCanceled; /** Keeps track of Progress made. */ private ProgressTicket progress; private int[] triangles; private ArrayWrapper[] network; private int K; private int N; private double[] nodeClustering; private int totalTriangles; public ClusteringCoefficient() { GraphController graphController = Lookup.getDefault().lookup(GraphController.class); if (graphController != null && graphController.getModel() != null) { isDirected = graphController.getModel().isDirected(); } } public double getAverageClusteringCoefficient() { return avgClusteringCoeff; } public void execute(GraphModel graphModel, AttributeModel attributeModel) { HierarchicalGraph hgraph = null; if (isDirected) { hgraph = graphModel.getHierarchicalDirectedGraphVisible(); } else { hgraph = graphModel.getHierarchicalUndirectedGraphVisible(); } execute(hgraph, attributeModel); } public void execute(HierarchicalGraph hgraph, AttributeModel attributeModel) { isCanceled = false; if (isDirected) bruteForce(hgraph, attributeModel); else triangles(hgraph); //Set results in columns AttributeTable nodeTable = attributeModel.getNodeTable(); AttributeColumn clusteringCol = nodeTable.getColumn(CLUSTERING_COEFF); if (clusteringCol == null) { clusteringCol = nodeTable.addColumn(CLUSTERING_COEFF, "Clustering Coefficient", AttributeType.DOUBLE, AttributeOrigin.COMPUTED, new Double(0)); } AttributeColumn triCount = null; if (!isDirected) { triCount = nodeTable.getColumn("Triangles"); if (triCount == null) { triCount = nodeTable.addColumn("Triangles", "Number of triangles", AttributeType.INT, AttributeOrigin.COMPUTED, new Integer(0)); } } for (int v = 0; v < N; v++) { if (network[v].length() > 1) { AttributeRow row = (AttributeRow) network[v].node.getNodeData().getAttributes(); row.setValue(clusteringCol, nodeClustering[v]); if (!isDirected) row.setValue(triCount, triangles[v]); } } } private int closest_in_array(int v) { int right = network[v].length() - 1; /* optimization for extreme cases */ if (right < 0) { return (-1); } if (network[v].get(0) >= v) { return (-1); } if (network[v].get(right) < v) { return (right); } if (network[v].get(right) == v) { return (right - 1); } int left = 0, mid; while (right > left) { mid = (left + right) / 2; if (v < network[v].get(mid)) { right = mid - 1; } else if (v > network[v].get(mid)) { left = mid + 1; } else { return (mid - 1); } } if (v > network[v].get(right)) { return (right); } else { return right - 1; } } /** * * @param v - The specific node to count the triangles on. */ private void newVertex(int v) { int[] A = new int[N]; for (int i = network[v].length() - 1; (i >= 0) && (network[v].get(i) > v); i--) { int neighbor = network[v].get(i); A[neighbor] = network[v].getCount(i); } for (int i = network[v].length() - 1; i >= 0; i--) { int neighbor = network[v].get(i); for (int j = closest_in_array(neighbor); j >= 0; j--) { int next = network[neighbor].get(j); if (A[next] > 0) { triangles[next] += network[v].getCount(i); triangles[v] += network[v].getCount(i); triangles[neighbor] += A[next]; } } } } private void tr_link_nohigh(int u, int v, int count) { int iu = 0, iv = 0, w; while ((iu < network[u].length()) && (iv < network[v].length())) { if (network[u].get(iu) < network[v].get(iv)) { iu++; } else if (network[u].get(iu) > network[v].get(iv)) { iv++; } else { /* neighbor in common */ w = network[u].get(iu); if (w >= K) { triangles[w] += count; } iu++; iv++; } } } public void triangles(HierarchicalGraph hgraph) { int ProgressCount = 0; Progress.start(progress, 7 * hgraph.getNodeCount()); hgraph.readLock(); N = hgraph.getNodeCount(); nodeClustering = new double[N]; /** Create network for processing */ network = new ArrayWrapper[N]; /** */ HashMap<Node, Integer> indicies = new HashMap<Node, Integer>(); int index = 0; for (Node s : hgraph.getNodes()) { indicies.put(s, index); network[index] = new ArrayWrapper(); index++; Progress.progress(progress, ++ProgressCount); } index = 0; for (Node node : hgraph.getNodes()) { HashMap<Node, EdgeWrapper> neighborTable = new HashMap<Node, EdgeWrapper>(); if (!isDirected) { for (Edge edge : hgraph.getEdgesAndMetaEdges(node)) { Node neighbor = hgraph.getOpposite(node, edge); neighborTable.put(neighbor, new EdgeWrapper(1, network[indicies.get(neighbor)])); } } else { for (Edge in : ((HierarchicalDirectedGraph) hgraph).getInEdgesAndMetaInEdges(node)) { Node neighbor = in.getSource().getNodeData().getNode(hgraph.getView().getViewId()); neighborTable.put(neighbor, new EdgeWrapper(1, network[indicies.get(neighbor)])); } for (Edge out : ((HierarchicalDirectedGraph) hgraph).getOutEdgesAndMetaOutEdges(node)) { Node neighbor = out.getTarget().getNodeData().getNode(hgraph.getView().getViewId()); EdgeWrapper ew = neighborTable.get(neighbor); if (ew == null) { neighborTable.put(neighbor, new EdgeWrapper(1, network[indicies.get(neighbor)])); } else { ew.count++; } } } EdgeWrapper[] edges = new EdgeWrapper[neighborTable.size()]; int i = 0; for (EdgeWrapper e : neighborTable.values()) { edges[i] = e; i++; } network[index].node = node; network[index].setArray(edges); index++; Progress.progress(progress, ++ProgressCount); if (isCanceled) { hgraph.readUnlockAll(); return; } } Arrays.sort(network); for (int j = 0; j < N; j++) { network[j].setID(j); Progress.progress(progress, ++ProgressCount); } for (int j = 0; j < N; j++) { Arrays.sort(network[j].getArray(), new Renumbering()); Progress.progress(progress, ++ProgressCount); } triangles = new int[N]; K = (int) Math.sqrt(N); for (int v = 0; v < K && v < N; v++) { newVertex(v); Progress.progress(progress, ++ProgressCount); } /* remaining links */ for (int v = N - 1; (v >= 0) && (v >= K); v--) { for (int i = closest_in_array(v); i >= 0; i--) { int u = network[v].get(i); if (u >= K) { tr_link_nohigh(u, v, network[v].getCount(i)); } } Progress.progress(progress, ++ProgressCount); if (isCanceled) { hgraph.readUnlockAll(); return; } } //Results and average avgClusteringCoeff = 0; totalTriangles = 0; int numNodesDegreeGreaterThanOne = 0; for (int v = 0; v < N; v++) { if (network[v].length() > 1) { numNodesDegreeGreaterThanOne++; double cc = triangles[v]; totalTriangles += triangles[v]; cc /= (network[v].length() * (network[v].length() - 1)); if (!isDirected) { cc *= 2.0f; } nodeClustering[v] = cc; avgClusteringCoeff += cc; } Progress.progress(progress, ++ProgressCount); if (isCanceled) { hgraph.readUnlockAll(); return; } } totalTriangles /= 3; avgClusteringCoeff /= numNodesDegreeGreaterThanOne; hgraph.readUnlock(); } private void bruteForce(HierarchicalGraph hgraph, AttributeModel attributeModel) { //The atrributes computed by the statistics AttributeTable nodeTable = attributeModel.getNodeTable(); AttributeColumn clusteringCol = nodeTable.getColumn("clustering"); if (clusteringCol == null) { clusteringCol = nodeTable.addColumn("clustering", "Clustering Coefficient", AttributeType.DOUBLE, AttributeOrigin.COMPUTED, new Double(0)); } float totalCC = 0; hgraph.readLock(); Progress.start(progress, hgraph.getNodeCount()); int node_count = 0; for (Node node : hgraph.getNodes()) { float nodeCC = 0; int neighborhood = 0; NodeIterable neighbors1 = hgraph.getNeighbors(node); for (Node neighbor1 : neighbors1) { neighborhood++; NodeIterable neighbors2 = hgraph.getNeighbors(node); for (Node neighbor2 : neighbors2) { if (neighbor1 == neighbor2) { continue; } if (isDirected) { if (((HierarchicalDirectedGraph) hgraph).getEdge(neighbor1, neighbor2) != null) { nodeCC++; } if (((HierarchicalDirectedGraph) hgraph).getEdge(neighbor2, neighbor1) != null) { nodeCC++; } } else { if (hgraph.isAdjacent(neighbor1, neighbor2)) { nodeCC++; } } } } nodeCC /= 2.0; if (neighborhood > 1) { float cc = nodeCC / (.5f * neighborhood * (neighborhood - 1)); if (isDirected) { cc = nodeCC / (neighborhood * (neighborhood - 1)); } AttributeRow row = (AttributeRow) node.getNodeData().getAttributes(); row.setValue(clusteringCol, cc); totalCC += cc; } if (isCanceled) { break; } node_count++; Progress.progress(progress, node_count); } avgClusteringCoeff = totalCC / hgraph.getNodeCount(); hgraph.readUnlockAll(); } public String getReport() { //distribution of values Map<Double, Integer> dist = new HashMap<Double, Integer>(); for (int i = 0; i < N; i++) { Double d = nodeClustering[i]; if (dist.containsKey(d)) { Integer v = dist.get(d); dist.put(d, v + 1); } else { dist.put(d, 1); } } //Distribution series XYSeries dSeries = ChartUtils.createXYSeries(dist, "Clustering Coefficient"); XYSeriesCollection dataset = new XYSeriesCollection(); dataset.addSeries(dSeries); JFreeChart chart = ChartFactory.createScatterPlot("Clustering Coefficient Distribution", "Value", "Count", dataset, PlotOrientation.VERTICAL, true, false, false); chart.removeLegend(); ChartUtils.decorateChart(chart); ChartUtils.scaleChart(chart, dSeries, false); String imageFile = ChartUtils.renderChart(chart, "clustering-coefficient.png"); NumberFormat f = new DecimalFormat("#0.000"); if (isDirected) { return "<HTML> <BODY> <h1> Clustering Coefficient Metric Report </h1> " + "<hr>" + "<br />" + "<h2> Parameters: </h2>" + "Network Interpretation: " + (isDirected ? "directed" : "undirected") + "<br />" + "<br>" + "<h2> Results: </h2>" + "Average Clustering Coefficient: " + f.format(avgClusteringCoeff) + "<br />" + "The Average Clustering Coefficient is the mean value of individual coefficients.<br /><br />" + imageFile + "<br /><br />" + "<h2> Algorithm: </h2>" + "Simple and slow brute force.<br />" + "</BODY> </HTML>"; } else { return "<HTML> <BODY> <h1> Clustering Coefficient Metric Report </h1> " + "<hr>" + "<br />" + "<h2> Parameters: </h2>" + "Network Interpretation: " + (isDirected ? "directed" : "undirected") + "<br />" + "<br>" + "<h2> Results: </h2>" + "Average Clustering Coefficient: " + f.format(avgClusteringCoeff) + "<br />" + "Total triangles: " + totalTriangles + "<br />" + "The Average Clustering Coefficient is the mean value of individual coefficients.<br /><br />" + imageFile + "<br /><br />" + "<h2> Algorithm: </h2>" + "Matthieu Latapy, <i>Main-memory Triangle Computations for Very Large (Sparse (Power-Law)) Graphs</i>, in Theoretical Computer Science (TCS) 407 (1-3), pages 458-473, 2008<br />" + "</BODY> </HTML>"; } } public void setDirected(boolean isDirected) { this.isDirected = isDirected; } public boolean isDirected() { return isDirected; } public boolean cancel() { isCanceled = true; return true; } public void setProgressTicket(ProgressTicket ProgressTicket) { this.progress = ProgressTicket; } public double[] getCoefficientReuslts() { double[] res = new double[N]; for (int v = 0; v < N; v++) { if (network[v].length() > 1) { res[v] = nodeClustering[v]; } } return res; } public double[] getTriangesReuslts() { double[] res = new double[N]; for (int v = 0; v < N; v++) { if (network[v].length() > 1) { res[v] = triangles[v]; } } return res; } }