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[FLINK-3907] [gelly] Directed Clustering Coefficient
This closes apache#2079
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...k-gelly-examples/src/main/java/org/apache/flink/graph/examples/ClusteringCoefficient.java
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| /* | ||
| * 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. | ||
| */ | ||
|
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| package org.apache.flink.graph.examples; | ||
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| import org.apache.commons.lang3.StringEscapeUtils; | ||
| import org.apache.commons.lang3.text.WordUtils; | ||
| import org.apache.commons.math3.random.JDKRandomGenerator; | ||
| import org.apache.flink.api.common.JobExecutionResult; | ||
| import org.apache.flink.api.java.DataSet; | ||
| import org.apache.flink.api.java.ExecutionEnvironment; | ||
| import org.apache.flink.api.java.io.CsvOutputFormat; | ||
| import org.apache.flink.api.java.utils.DataSetUtils; | ||
| import org.apache.flink.api.java.utils.ParameterTool; | ||
| import org.apache.flink.graph.Graph; | ||
| import org.apache.flink.graph.GraphAnalytic; | ||
| import org.apache.flink.graph.asm.translate.LongValueToIntValue; | ||
| import org.apache.flink.graph.asm.translate.TranslateGraphIds; | ||
| import org.apache.flink.graph.generator.RMatGraph; | ||
| import org.apache.flink.graph.generator.random.JDKRandomGeneratorFactory; | ||
| import org.apache.flink.graph.generator.random.RandomGenerableFactory; | ||
| import org.apache.flink.types.IntValue; | ||
| import org.apache.flink.types.LongValue; | ||
| import org.apache.flink.types.NullValue; | ||
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| import java.text.NumberFormat; | ||
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| /** | ||
| * Driver for the library implementations of Global and Local Clustering Coefficient. | ||
| * | ||
| * This example reads a simple directed or undirected graph from a CSV file or | ||
| * generates an RMat graph with the given scale and edge factor then calculates | ||
| * the local clustering coefficient for each vertex and the global clustering | ||
| * coefficient for the graph. | ||
| * | ||
| * @see org.apache.flink.graph.library.clustering.directed.GlobalClusteringCoefficient | ||
| * @see org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient | ||
| * @see org.apache.flink.graph.library.clustering.undirected.GlobalClusteringCoefficient | ||
| * @see org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient | ||
| */ | ||
| public class ClusteringCoefficient { | ||
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| public static final int DEFAULT_SCALE = 10; | ||
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| public static final int DEFAULT_EDGE_FACTOR = 16; | ||
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| public static final boolean DEFAULT_CLIP_AND_FLIP = true; | ||
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| private static void printUsage() { | ||
| System.out.println(WordUtils.wrap("The local clustering coefficient measures the connectedness of each" + | ||
| " vertex's neighborhood and the global clustering coefficient measures the connectedness of the graph." + | ||
| " Scores range from 0.0 (no edges between neighbors or vertices) to 1.0 (neighborhood or graph" + | ||
| " is a clique).", 80)); | ||
| System.out.println(); | ||
| System.out.println(WordUtils.wrap("This algorithm returns tuples containing the vertex ID, the degree of" + | ||
| " the vertex, and the number of edges between vertex neighbors.", 80)); | ||
| System.out.println(); | ||
| System.out.println("usage: ClusteringCoefficient --directed <true | false> --input <csv | rmat [options]> --output <print | hash | csv [options]"); | ||
| System.out.println(); | ||
| System.out.println("options:"); | ||
| System.out.println(" --input csv --input_filename FILENAME [--input_line_delimiter LINE_DELIMITER] [--input_field_delimiter FIELD_DELIMITER]"); | ||
| System.out.println(" --input rmat [--scale SCALE] [--edge_factor EDGE_FACTOR]"); | ||
| System.out.println(); | ||
| System.out.println(" --output print"); | ||
| System.out.println(" --output hash"); | ||
| System.out.println(" --output csv --output_filename FILENAME [--output_line_delimiter LINE_DELIMITER] [--output_field_delimiter FIELD_DELIMITER]"); | ||
| } | ||
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| public static void main(String[] args) throws Exception { | ||
| // Set up the execution environment | ||
| final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); | ||
| env.getConfig().enableObjectReuse(); | ||
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| ParameterTool parameters = ParameterTool.fromArgs(args); | ||
| if (! parameters.has("directed")) { | ||
| printUsage(); | ||
| return; | ||
| } | ||
| boolean directedAlgorithm = parameters.getBoolean("directed"); | ||
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| // global and local clustering coefficient results | ||
| GraphAnalytic gcc; | ||
| DataSet lcc; | ||
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| switch (parameters.get("input", "")) { | ||
| case "csv": { | ||
| String lineDelimiter = StringEscapeUtils.unescapeJava( | ||
| parameters.get("input_line_delimiter", CsvOutputFormat.DEFAULT_LINE_DELIMITER)); | ||
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| String fieldDelimiter = StringEscapeUtils.unescapeJava( | ||
| parameters.get("input_field_delimiter", CsvOutputFormat.DEFAULT_FIELD_DELIMITER)); | ||
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| Graph<LongValue, NullValue, NullValue> graph = Graph | ||
| .fromCsvReader(parameters.get("input_filename"), env) | ||
| .ignoreCommentsEdges("#") | ||
| .lineDelimiterEdges(lineDelimiter) | ||
| .fieldDelimiterEdges(fieldDelimiter) | ||
| .keyType(LongValue.class); | ||
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| if (directedAlgorithm) { | ||
| gcc = graph | ||
| .run(new org.apache.flink.graph.library.clustering.directed.GlobalClusteringCoefficient<LongValue, NullValue, NullValue>()); | ||
| lcc = graph | ||
| .run(new org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient<LongValue, NullValue, NullValue>()); | ||
| } else { | ||
| gcc = graph | ||
| .run(new org.apache.flink.graph.library.clustering.undirected.GlobalClusteringCoefficient<LongValue, NullValue, NullValue>()); | ||
| lcc = graph | ||
| .run(new org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient<LongValue, NullValue, NullValue>()); | ||
| } | ||
| } break; | ||
|
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| case "rmat": { | ||
| int scale = parameters.getInt("scale", DEFAULT_SCALE); | ||
| int edgeFactor = parameters.getInt("edge_factor", DEFAULT_EDGE_FACTOR); | ||
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| RandomGenerableFactory<JDKRandomGenerator> rnd = new JDKRandomGeneratorFactory(); | ||
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| long vertexCount = 1L << scale; | ||
| long edgeCount = vertexCount * edgeFactor; | ||
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| Graph<LongValue, NullValue, NullValue> graph = new RMatGraph<>(env, rnd, vertexCount, edgeCount) | ||
| .generate(); | ||
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| if (directedAlgorithm) { | ||
| if (scale > 32) { | ||
| Graph<LongValue, NullValue, NullValue> newGraph = graph | ||
| .run(new org.apache.flink.graph.asm.simple.directed.Simplify<LongValue, NullValue, NullValue>()); | ||
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| gcc = newGraph | ||
| .run(new org.apache.flink.graph.library.clustering.directed.GlobalClusteringCoefficient<LongValue, NullValue, NullValue>()); | ||
| lcc = newGraph | ||
| .run(new org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient<LongValue, NullValue, NullValue>()); | ||
| } else { | ||
| Graph<IntValue, NullValue, NullValue> newGraph = graph | ||
| .run(new TranslateGraphIds<LongValue, IntValue, NullValue, NullValue>(new LongValueToIntValue())) | ||
| .run(new org.apache.flink.graph.asm.simple.directed.Simplify<IntValue, NullValue, NullValue>()); | ||
|
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| gcc = newGraph | ||
| .run(new org.apache.flink.graph.library.clustering.directed.GlobalClusteringCoefficient<IntValue, NullValue, NullValue>()); | ||
| lcc = newGraph | ||
| .run(new org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient<IntValue, NullValue, NullValue>()); | ||
| } | ||
| } else { | ||
| boolean clipAndFlip = parameters.getBoolean("clip_and_flip", DEFAULT_CLIP_AND_FLIP); | ||
|
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| if (scale > 32) { | ||
| Graph<LongValue, NullValue, NullValue> newGraph = graph | ||
| .run(new org.apache.flink.graph.asm.simple.undirected.Simplify<LongValue, NullValue, NullValue>(clipAndFlip)); | ||
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| gcc = newGraph | ||
| .run(new org.apache.flink.graph.library.clustering.undirected.GlobalClusteringCoefficient<LongValue, NullValue, NullValue>()); | ||
| lcc = newGraph | ||
| .run(new org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient<LongValue, NullValue, NullValue>()); | ||
| } else { | ||
| Graph<IntValue, NullValue, NullValue> newGraph = graph | ||
| .run(new TranslateGraphIds<LongValue, IntValue, NullValue, NullValue>(new LongValueToIntValue())) | ||
| .run(new org.apache.flink.graph.asm.simple.undirected.Simplify<IntValue, NullValue, NullValue>(clipAndFlip)); | ||
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| gcc = newGraph | ||
| .run(new org.apache.flink.graph.library.clustering.undirected.GlobalClusteringCoefficient<IntValue, NullValue, NullValue>()); | ||
| lcc = newGraph | ||
| .run(new org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient<IntValue, NullValue, NullValue>()); | ||
| } | ||
| } | ||
| } break; | ||
|
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| default: | ||
| printUsage(); | ||
| return; | ||
| } | ||
|
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| switch (parameters.get("output", "")) { | ||
| case "print": | ||
| if (directedAlgorithm) { | ||
| for (Object e: lcc.collect()) { | ||
| org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient.Result result = | ||
| (org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient.Result)e; | ||
| System.out.println(result.toVerboseString()); | ||
| } | ||
| } else { | ||
| for (Object e: lcc.collect()) { | ||
| org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient.Result result = | ||
| (org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient.Result)e; | ||
| System.out.println(result.toVerboseString()); | ||
| } | ||
| } | ||
| System.out.println(gcc.getResult()); | ||
| break; | ||
|
|
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| case "hash": | ||
| System.out.println(DataSetUtils.checksumHashCode(lcc)); | ||
| System.out.println(gcc.getResult()); | ||
| break; | ||
|
|
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| case "csv": | ||
| String filename = parameters.get("output_filename"); | ||
|
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| String lineDelimiter = StringEscapeUtils.unescapeJava( | ||
| parameters.get("output_line_delimiter", CsvOutputFormat.DEFAULT_LINE_DELIMITER)); | ||
|
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| String fieldDelimiter = StringEscapeUtils.unescapeJava( | ||
| parameters.get("output_field_delimiter", CsvOutputFormat.DEFAULT_FIELD_DELIMITER)); | ||
|
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| lcc.writeAsCsv(filename, lineDelimiter, fieldDelimiter); | ||
|
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| System.out.println(gcc.execute()); | ||
| break; | ||
|
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| default: | ||
| printUsage(); | ||
| return; | ||
| } | ||
|
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| JobExecutionResult result = env.getLastJobExecutionResult(); | ||
|
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| NumberFormat nf = NumberFormat.getInstance(); | ||
| System.out.println("Execution runtime: " + nf.format(result.getNetRuntime()) + " ms"); | ||
| } | ||
| } |
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