| Description |
1 online resource (1 volume) : illustrations |
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text file |
| Series |
Community experience distilled |
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Community experience distilled.
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| Bibliography |
Includes bibliographical references and index. |
| Contents |
Cover; Copyright; Credits; Foreword; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Spark and GraphX; Downloading and installing Spark 1.4.1; Experimenting with the Spark shell; Getting started with GraphX; Building a tiny social network; Loading the data; The property graph; Transforming RDDs to VertexRDD and EdgeRDD; Introducing graph operations; Building and submitting a standalone application; Writing and configuring a Spark program; Building the program with the Scala Build Tool; Deploying and running with spark-submit. |
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The graph visualizationInstalling the GraphStream and BreezeViz libraries; Visualizing the graph data; Plotting the degree distribution; The analysis of network connectedness; Finding the connected components; Counting triangles and computing clustering coefficients; The network centrality and PageRank; How PageRank works; Ranking web pages; Scala Build Tool revisited; Organizing build definitions; Managing library dependencies; A preview of the steps; Running tasks with SBT commands; Summary; Chapter 4: Transforming and Shaping Up Graphs to Your Needs. |
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Transforming the vertex and edge attributesmapVertices; mapEdges; mapTriplets; Modifying graph structures; The reverse operator; The subgraph operator; The mask operator; The groupEdges operator; Joining graph datasets; joinVertices; outerJoinVertices; Example -- Hollywood movie graph; Data operations on VertexRDD and EdgeRDD; Mapping VertexRDD and EdgeRDD; Filtering VertexRDDs; Joining VertexRDDs; Joining EdgeRDDs; Reversing edge directions; Collecting neighboring information; Example -- from food network to flavor pairing; Summary; Chapter 5: Creating Custom Graph Aggregation Operators. |
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NCAA College Basketball datasetsThe aggregateMessages operator; EdgeContext; Abstracting out the aggregation; Keeping things DRY; Coach wants more numbers; Calculating average points per game; Defense stats -- D matters as in direction; Joining average stats into a graph; Performance optimization; The MapReduceTriplets operator; Summary; Chapter 6: Iterative Graph-Parallel Processing with Pregel; The Pregel computational model; Example -- iterating towards the social equality; The Pregel API in GraphX; Community detection through label propagation; The Pregel implementation of PageRank; Summary. |
| Summary |
Chapter 2: Building and Exploring Graphs; Network datasets; The communication network; Flavor networks; Social ego networks; Graph builders; The Graph factory method; edgeListFile; fromEdges; fromEdgeTuples; Building graphs; Building directed graphs; Building a bipartite graph; Building a weighted social ego network; Computing the degrees of the network nodes; In-degree and out-degree of the Enron email network; Degrees in the bipartite food network; Degree histogram of the social ego networks; Summary; Chapter 3: Graph Analysis and Visualization; Network datasets. |
| Language |
English. |
| Subject |
Spark (Electronic resource : Apache Software Foundation)
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Spark (Electronic resource : Apache Software Foundation) |
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Graphic methods -- Computer programs.
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Electronic data processing.
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Electronic data processing |
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Graphic methods -- Computer programs |
| Added Author |
Lee, Denny, author of introduction, etc.
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| Added Title |
Build, process, and analyze large-scale graphs with Spark |
| Other Form: |
Print version: Ramamonjison, Rindra. Apache Spark Graph Processing. Birmingham : Packt Publishing Ltd, ©2015 9781784391805 |
| ISBN |
9781784398958 (electronic bk.) |
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1784398950 (electronic bk.) |
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