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https://issues.apache.org/jira/browse/FLINK-2310?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14619228#comment-14619228
]
ASF GitHub Bot commented on FLINK-2310:
---------------------------------------
Github user andralungu commented on a diff in the pull request:
https://github.com/apache/flink/pull/892#discussion_r34189267
--- Diff:
flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/AdamicAdarSimilarityMeasure.java
---
@@ -0,0 +1,243 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+package org.apache.flink.graph.example;
+import org.apache.flink.api.common.ProgramDescription;
+import org.apache.flink.api.common.functions.MapFunction;
+import org.apache.flink.api.java.DataSet;
+import org.apache.flink.api.java.ExecutionEnvironment;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.graph.Edge;
+import org.apache.flink.graph.EdgeDirection;
+import org.apache.flink.graph.Graph;
+import org.apache.flink.graph.ReduceNeighborsFunction;
+import org.apache.flink.graph.Vertex;
+import org.apache.flink.graph.Triplet;
+import
org.apache.flink.graph.example.utils.AdamicAdarSimilarityMeasureData;
+import java.util.HashSet;
+
+/**
+ * Given a directed, unweighted graph, return a weighted graph where the
edge values are equal
+ * to the Adamic Acard similarity coefficient which is given as
+ * Summation of weights of common neighbors of the source and destination
vertex
+ *The weights are given as 1/log(nK) nK is the degree or the vertex
+ * See <a
href="http://social.cs.uiuc.edu/class/cs591kgk/friendsadamic.pdf";>Friends and
neighbors on the Web</a>
+ *
+ * <p>
+ * Input files are plain text files and must be formatted as follows:
+ * <br>
+ * Edges are represented by pairs of srcVertexId, trgVertexId
separated by tabs.
+ * Edges themselves are separated by newlines.
+ * For example: <code>1 2\n1 3\n</code> defines two edges
1-2 and 1-3.
+ * </p>
+ *
+ * Usage <code> AdamicAdarSimilarityMeasure <edge path> <result
path></code><br>
+ * If no parameters are provided, the program is run with default data from
+ * {@link
org.apache.flink.graph.example.utils.AdamicAdarSimilarityMeasureData}
+ */
+@SuppressWarnings("serial")
+public class AdamicAdarSimilarityMeasure implements ProgramDescription {
+
+
+ public static void main(String[] args) throws Exception {
+
+ if(!parseParameters(args)) {
+ return;
+ }
+
+ ExecutionEnvironment env =
ExecutionEnvironment.getExecutionEnvironment();
+
+ DataSet<Edge<Long, Double>> edges =
AdamicAdarSimilarityMeasure.getEdgesDataSet(env);
+
+ /*Graph is generated without vertex weights. Vertices will have
a Tuple2 as value
+ where first field will be the weight of the vertex as 1/log(kn)
kn is the degree of the vertex
+ Second field is a HashSet whose elements are again Tuple2, of
which first field is the VertexID of the neighbor
+ and second field is the weight of that neighbor
+ */
+ Graph<Long, Tuple2<Double, HashSet<Tuple2<Long, Double>>>,
Double> graph =
+ Graph.fromDataSet(edges,new mapVertices(), env);
+
+ DataSet<Tuple2<Long, Long>> degrees = graph.getDegrees();
+
+ //vertices are given weights in graph
+ graph = graph.joinWithVertices(degrees,new
AssignWeightToVertices());
+
+ //neighbors are computed for all the vertices
+ DataSet<Tuple2<Long, Tuple2<Double, HashSet<Tuple2<Long,
Double>>>>> computedNeighbors =
+ graph.reduceOnNeighbors(new GatherNeighbors(),
EdgeDirection.ALL);
+
+ //graph is updated with the new vertex values
+ Graph<Long, Tuple2<Double, HashSet<Tuple2<Long, Double>>>,
Double> graphWithVertexValues =
+ graph.joinWithVertices(computedNeighbors, new
UpdateGraphVertices());
+
+ //edges are given Adamic Adar coefficient as value
+ DataSet<Edge<Long, Double>> edgesWithAdamicValues =
graphWithVertexValues.getTriplets().map(new ComputeAdamic());
+
+ // emit result
+ if (fileOutput) {
+ edgesWithAdamicValues.writeAsCsv(outputPath, "\n", ",");
+
+ // since file sinks are lazy, we trigger the execution
explicitly
+ env.execute("Executing Adamic Adar Similarity Measure");
+ } else {
+ edgesWithAdamicValues.print();
+ }
+ }
+
+ @Override
+ public String getDescription() {
+ return "Adamic Adar Similarity Measure";
+ }
+
+ /*
+ * Each vertex has a Tuple2 as value. The first field of the Tuple is
the weight of the vertex
+ * The second field is the HashSet containing of all the neighbors and
their weights
+ */
+ private static final class GatherNeighbors implements
ReduceNeighborsFunction<Tuple2<Double,
+
HashSet<Tuple2<Long, Double>>>>
{
+ @Override
+ public Tuple2<Double, HashSet<Tuple2<Long, Double>>>
+ reduceNeighbors(Tuple2<Double, HashSet<Tuple2<Long, Double>>>
first,
+ Tuple2<Double,
HashSet<Tuple2<Long, Double>>> second) {
+ first.f1.addAll(second.f1);
+ return first;
+ }
+ }
+
+ //mapfunction to initialise Vertex values from edge dataset
+ private static class mapVertices implements MapFunction<Long,
Tuple2<Double, HashSet<Tuple2<Long, Double>>>> {
--- End diff --
class name that starts with "m"? :)
> Add an Adamic-Adar Similarity example
> -------------------------------------
>
> Key: FLINK-2310
> URL: https://issues.apache.org/jira/browse/FLINK-2310
> Project: Flink
> Issue Type: Task
> Components: Gelly
> Reporter: Andra Lungu
> Assignee: Shivani Ghatge
> Priority: Minor
>
> Just as Jaccard, the Adamic-Adar algorithm measures the similarity between a
> set of nodes. However, instead of counting the common neighbors and dividing
> them by the total number of neighbors, the similarity is weighted according
> to the vertex degrees. In particular, it's equal to log(1/numberOfEdges).
> The Adamic-Adar algorithm can be broken into three steps:
> 1). For each vertex, compute the log of its inverse degrees (with the formula
> above) and set it as the vertex value.
> 2). Each vertex will then send this new computed value along with a list of
> neighbors to the targets of its out-edges
> 3). Weigh the edges with the Adamic-Adar index: Sum over n from CN of
> log(1/k_n)(CN is the set of all common neighbors of two vertices x, y. k_n is
> the degree of node n). See [2]
> Prerequisites:
> - Full understanding of the Jaccard Similarity Measure algorithm
> - Reading the associated literature:
> [1] http://social.cs.uiuc.edu/class/cs591kgk/friendsadamic.pdf
> [2]
> http://stackoverflow.com/questions/22565620/fast-algorithm-to-compute-adamic-adar
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