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/*
Copyright 2008-2011 Gephi
Authors : Patick J. McSweeney , Sebastien Heymann
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.
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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
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However, if you add GPL Version 3 code and therefore, elected the GPL
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Contributor(s):
Portions Copyrighted 2011 Gephi Consortium.
*/
package org.gephi.statistics.plugin;
import java.io.IOException;
import java.util.Arrays;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.ListIterator;
import java.util.Map;
import java.util.Stack;
import org.gephi.graph.api.Column;
import org.gephi.graph.api.DirectedGraph;
import org.gephi.graph.api.Edge;
import org.gephi.graph.api.EdgeIterable;
import org.gephi.graph.api.Graph;
import org.gephi.graph.api.GraphController;
import org.gephi.graph.api.GraphModel;
import org.gephi.graph.api.Node;
import org.gephi.graph.api.NodeIterable;
import org.gephi.graph.api.Table;
import org.gephi.statistics.spi.Statistics;
import org.gephi.utils.TempDirUtils;
import org.gephi.utils.TempDirUtils.TempDir;
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.Exceptions;
import org.openide.util.Lookup;
/**
* Ref: Ulrik Brandes, A Faster Algorithm for Betweenness Centrality, in Journal of Mathematical Sociology 25(2):163-177, (2001)
*
* @author pjmcswee
* @author Jonny Wray
*/
public class GraphDistance implements Statistics, LongTask {
public static final String BETWEENNESS = "betweenesscentrality";
public static final String CLOSENESS = "closnesscentrality";
public static final String HARMONIC_CLOSENESS = "harmonicclosnesscentrality";
public static final String ECCENTRICITY = "eccentricity";
/**
*
*/
private double[] betweenness;
/**
*
*/
private double[] closeness;
private double[] harmonicCloseness;
/**
*
*/
private double[] eccentricity;
/**
*
*/
private int diameter;
private int radius;
/**
*
*/
private double avgDist;
/**
*
*/
private int N;
/**
*
*/
private boolean isDirected;
/**
*
*/
private ProgressTicket progress;
/**
*
*/
private boolean isCanceled;
private boolean isNormalized;
/**
* Construct a GraphDistance calculator for the current graph model
*/
public GraphDistance() {
GraphController graphController = Lookup.getDefault().lookup(GraphController.class);
if (graphController != null && graphController.getGraphModel() != null) {
isDirected = graphController.getGraphModel().isDirected();
}
}
/**
* Gets the average shortest path length in the network
*
* @return average shortest path length for all nodes
*/
public double getPathLength() {
return avgDist;
}
/**
* @return the diameter of the network
*/
public double getDiameter() {
return diameter;
}
/**
* @return the radius of the network
*/
public double getRadius() {
return radius;
}
/**
* @param graphModel
*/
@Override
public void execute(GraphModel graphModel) {
Graph graph;
if (isDirected) {
graph = graphModel.getDirectedGraphVisible();
} else {
graph = graphModel.getUndirectedGraphVisible();
}
execute(graph);
}
public void execute(Graph graph) {
isCanceled = false;
initializeAttributeColunms(graph.getModel());
graph.readLock();
try {
N = graph.getNodeCount();
initializeStartValues();
HashMap indicies = createIndiciesMap(graph);
Map metrics = calculateDistanceMetrics(graph, indicies, isDirected, isNormalized);
eccentricity = metrics.get(ECCENTRICITY);
closeness = metrics.get(CLOSENESS);
harmonicCloseness = metrics.get(HARMONIC_CLOSENESS);
betweenness = metrics.get(BETWEENNESS);
saveCalculatedValues(graph, indicies, eccentricity, betweenness, closeness, harmonicCloseness);
} finally {
graph.readUnlock();
}
}
public Map calculateDistanceMetrics(Graph graph, HashMap indicies,
boolean directed, boolean normalized) {
int n = graph.getNodeCount();
HashMap metrics = new HashMap<>();
double[] nodeEccentricity = new double[n];
double[] nodeBetweenness = new double[n];
double[] nodeCloseness = new double[n];
double[] nodeHarmonicCloseness = new double[n];
metrics.put(ECCENTRICITY, nodeEccentricity);
metrics.put(CLOSENESS, nodeCloseness);
metrics.put(HARMONIC_CLOSENESS, nodeHarmonicCloseness);
metrics.put(BETWEENNESS, nodeBetweenness);
Progress.start(progress, graph.getNodeCount());
int count = 0;
int totalPaths = 0;
NodeIterable nodesIterable = graph.getNodes();
for (Node s : nodesIterable) {
Stack S = new Stack<>();
LinkedList[] P = new LinkedList[n];
double[] theta = new double[n];
int[] d = new int[n];
int s_index = indicies.get(s);
setInitParametetrsForNode(s, P, theta, d, s_index, n);
LinkedList Q = new LinkedList<>();
Q.addLast(s);
while (!Q.isEmpty()) {
Node v = Q.removeFirst();
S.push(v);
int v_index = indicies.get(v);
EdgeIterable edgeIter = getEdgeIter(graph, v, directed);
for (Edge edge : edgeIter) {
Node reachable = graph.getOpposite(v, edge);
int r_index = indicies.get(reachable);
if (d[r_index] < 0) {
Q.addLast(reachable);
d[r_index] = d[v_index] + 1;
}
if (d[r_index] == (d[v_index] + 1)) {
theta[r_index] = theta[r_index] + theta[v_index];
P[r_index].addLast(v);
}
}
}
double reachable = 0;
for (int i = 0; i < n; i++) {
if (d[i] > 0) {
avgDist += d[i];
nodeEccentricity[s_index] = (int) Math.max(nodeEccentricity[s_index], d[i]);
nodeCloseness[s_index] += d[i];
nodeHarmonicCloseness[s_index] += Double.isInfinite(d[i]) ? 0.0 : 1.0 / d[i];
diameter = Math.max(diameter, d[i]);
reachable++;
}
}
radius = (int) Math.min(nodeEccentricity[s_index], radius);
if (reachable != 0) {
nodeCloseness[s_index] = (nodeCloseness[s_index] == 0) ? 0 : reachable / nodeCloseness[s_index];
nodeHarmonicCloseness[s_index] = nodeHarmonicCloseness[s_index] / reachable;
}
totalPaths += reachable;
double[] delta = new double[n];
while (!S.empty()) {
Node w = S.pop();
int w_index = indicies.get(w);
ListIterator iter1 = P[w_index].listIterator();
while (iter1.hasNext()) {
Node u = iter1.next();
int u_index = indicies.get(u);
delta[u_index] += (theta[u_index] / theta[w_index]) * (1 + delta[w_index]);
}
if (w != s) {
nodeBetweenness[w_index] += delta[w_index];
}
}
count++;
if (isCanceled) {
nodesIterable.doBreak();
return metrics;
}
Progress.progress(progress, count);
}
avgDist /= totalPaths;//mN * (mN - 1.0f);
calculateCorrection(graph, indicies, nodeBetweenness, directed, normalized);
return metrics;
}
private void setInitParametetrsForNode(Node s, LinkedList[] P, double[] theta, int[] d, int index, int n) {
for (int j = 0; j < n; j++) {
P[j] = new LinkedList<>();
theta[j] = 0;
d[j] = -1;
}
theta[index] = 1;
d[index] = 0;
}
private EdgeIterable getEdgeIter(Graph graph, Node v, boolean directed) {
EdgeIterable edgeIter;
if (directed) {
edgeIter = ((DirectedGraph) graph).getOutEdges(v);
} else {
edgeIter = graph.getEdges(v);
}
return edgeIter;
}
private void initializeAttributeColunms(GraphModel graphModel) {
Table nodeTable = graphModel.getNodeTable();
ColumnUtils.cleanUpColumns(nodeTable, new String[] {ECCENTRICITY, CLOSENESS, HARMONIC_CLOSENESS, BETWEENNESS}, Double.class);
if (!nodeTable.hasColumn(ECCENTRICITY)) {
nodeTable.addColumn(ECCENTRICITY, "Eccentricity", Double.class, new Double(0));
}
if (!nodeTable.hasColumn(CLOSENESS)) {
nodeTable.addColumn(CLOSENESS, "Closeness Centrality", Double.class, new Double(0));
}
if (!nodeTable.hasColumn(HARMONIC_CLOSENESS)) {
nodeTable.addColumn(HARMONIC_CLOSENESS, "Harmonic Closeness Centrality", Double.class, new Double(0));
}
if (!nodeTable.hasColumn(BETWEENNESS)) {
nodeTable.addColumn(BETWEENNESS, "Betweenness Centrality", Double.class, new Double(0));
}
}
public HashMap createIndiciesMap(Graph graph) {
HashMap indicies = new HashMap<>();
int index = 0;
for (Node s : graph.getNodes()) {
indicies.put(s, index);
index++;
}
return indicies;
}
public void initializeStartValues() {
betweenness = new double[N];
eccentricity = new double[N];
closeness = new double[N];
harmonicCloseness = new double[N];
diameter = 0;
avgDist = 0;
radius = Integer.MAX_VALUE;
}
public double computeBetweennessNormalizationFactor(int nodeCount) {
return (nodeCount - 1.d) * (nodeCount - 2.d);
}
private void calculateCorrection(Graph graph, HashMap indicies,
double[] nodeBetweenness, boolean directed, boolean normalized) {
int n = graph.getNodeCount();
for (Node s : graph.getNodes()) {
int s_index = indicies.get(s);
if (!directed) {
nodeBetweenness[s_index] /= 2.d;
}
if (normalized) {
double betweennessNormalizationFactor = computeBetweennessNormalizationFactor(n);
if (!directed) {
betweennessNormalizationFactor /= 2;
}
nodeBetweenness[s_index] /= betweennessNormalizationFactor;
}
}
}
private void saveCalculatedValues(Graph graph, HashMap indicies,
double[] nodeEccentricity, double[] nodeBetweenness, double[] nodeCloseness,
double[] nodeHarmonicCloseness) {
for (Node s : graph.getNodes()) {
int s_index = indicies.get(s);
s.setAttribute(ECCENTRICITY, nodeEccentricity[s_index]);
s.setAttribute(CLOSENESS, nodeCloseness[s_index]);
s.setAttribute(HARMONIC_CLOSENESS, nodeHarmonicCloseness[s_index]);
s.setAttribute(BETWEENNESS, nodeBetweenness[s_index]);
}
}
public boolean isNormalized() {
return isNormalized;
}
public void setNormalized(boolean isNormalized) {
this.isNormalized = isNormalized;
}
public boolean isDirected() {
return isDirected;
}
public void setDirected(boolean isDirected) {
this.isDirected = isDirected;
}
private String createImageFile(TempDir tempDir, double[] pVals, String pName, String pX, String pY) {
//distribution of values
Map dist = new HashMap<>();
for (int i = 0; i < N; i++) {
Double d = pVals[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, pName);
XYSeriesCollection dataset = new XYSeriesCollection();
dataset.addSeries(dSeries);
JFreeChart chart = ChartFactory.createXYLineChart(
pName,
pX,
pY,
dataset,
PlotOrientation.VERTICAL,
true,
false,
false);
chart.removeLegend();
ChartUtils.decorateChart(chart);
ChartUtils.scaleChart(chart, dSeries, isNormalized);
return ChartUtils.renderChart(chart, pName + ".png");
}
/**
* @return
*/
@Override
public String getReport() {
String htmlIMG1 = "";
String htmlIMG2 = "";
String htmlIMG3 = "";
String htmlIMG4 = "";
try {
TempDir tempDir = TempDirUtils.createTempDir();
htmlIMG1 = createImageFile(tempDir, betweenness, "Betweenness Centrality Distribution", "Value", "Count");
htmlIMG2 = createImageFile(tempDir, closeness, "Closeness Centrality Distribution", "Value", "Count");
htmlIMG3 =
createImageFile(tempDir, harmonicCloseness, "Harmonic Closeness Centrality Distribution", "Value",
"Count");
htmlIMG4 = createImageFile(tempDir, eccentricity, "Eccentricity Distribution", "Value", "Count");
} catch (IOException ex) {
Exceptions.printStackTrace(ex);
}
String report = " Graph Distance Report
"
+ "
"
+ "
"
+ " Parameters:
"
+ "Network Interpretation: " + (isDirected ? "directed" : "undirected") + "
"
+ "
Results:
"
+ "Diameter: " + diameter + "
"
+ "Radius: " + radius + "
"
+ "Average Path length: " + avgDist + "
"
+ htmlIMG1 + "
"
+ htmlIMG2 + "
"
+ htmlIMG3 + "
"
+ htmlIMG4
+ "
" + " Algorithm:
"
+
"Ulrik Brandes, A Faster Algorithm for Betweenness Centrality, in Journal of Mathematical Sociology 25(2):163-177, (2001)
"
+ " ";
return report;
}
/**
* @return
*/
@Override
public boolean cancel() {
this.isCanceled = true;
return true;
}
/**
* @param progressTicket
*/
@Override
public void setProgressTicket(ProgressTicket progressTicket) {
this.progress = progressTicket;
}
}