1 files changed, 0 insertions, 64 deletions
diff --git a/src/Chapter4/util/EigenVectorScorer.java b/src/Chapter4/util/EigenVectorScorer.java
deleted file mode 100644
index da0c1a8..0000000
--- a/src/Chapter4/util/EigenVectorScorer.java
+++ /dev/null
@@ -1,64 +0,0 @@
-package Chapter4.util;
-
-import GraphElements.RetweetEdge;
-import GraphElements.UserNode;
-import cern.colt.matrix.DoubleMatrix2D;
-import cern.colt.matrix.impl.SparseDoubleMatrix2D;
-import cern.colt.matrix.linalg.EigenvalueDecomposition;
-import edu.uci.ics.jung.algorithms.scoring.VertexScorer;
-import edu.uci.ics.jung.graph.Hypergraph;
-
-/**
- * This is a Jung Node Scorer that computes the Eigenvector Centrality for each node.
- */
-public class EigenVectorScorer implements VertexScorer<UserNode, Double> {
-
-	private UserNode[] users;
-	private DoubleMatrix2D eigenVectors;
-	private int dominantEigenvectorIdx;
-
-	public EigenVectorScorer(Hypergraph<UserNode, RetweetEdge> graph){
-		users = new UserNode[graph.getVertexCount()];
-		graph.getVertices().toArray(users);
-		
-		/* Step 1: Create the adjacency matrix.
-		 * 
-		 * An adjacency matrix is a matrix with N users and N columns, 
-		 * where N is the number of nodes in the network.
-		 * An entry in the matrix is 1 when node i connects to node j,
-		 * and 0 otherwise.
-		 */
-		SparseDoubleMatrix2D matrix = new SparseDoubleMatrix2D(users.length, users.length);
-		for(int i = 0; i < users.length; i++){
-			for(int j = 0; j < users.length; j++){
-				matrix.setQuick(i, j, graph.containsEdge(new RetweetEdge(users[i], users[j])) ? 1 : 0);
-			}
-		}
-		
-		/* Step 2: Find the principle eigenvector.
-		 * For more information on eigen-decomposition please see
-		 * http://mathworld.wolfram.com/EigenDecomposition.html
-		 */
-		EigenvalueDecomposition eig = new EigenvalueDecomposition(matrix);
-		DoubleMatrix2D eigenVals = eig.getD();
-		eigenVectors = eig.getV();
-		
-		dominantEigenvectorIdx = 0;
-		for(int i = 1; i < eigenVals.columns(); i++){
-			if(eigenVals.getQuick(dominantEigenvectorIdx, dominantEigenvectorIdx) <
-					eigenVals.getQuick(i, i)){
-				dominantEigenvectorIdx = i;
-			}
-		}
-	}
-	
-	public Double getVertexScore(UserNode arg0) {
-		for(int i = 0; i < users.length; i++){
-			if(users[i].equals(arg0)){
-				return Math.abs(eigenVectors.getQuick(i, dominantEigenvectorIdx));
-			}
-		}
-		return null;
-	}
-
-}