Apache spark graph processing : build, process, and analyze large-scale graphs with Spark /

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Bibliographic Details
Author / Creator:Ramamonjison, Rindra, author.
Imprint:Birmingham, UK : Packt Publishing, [2015]
©2015
Description:1 online resource (1 volume) : illustrations
Language:English
Series:Community experience distilled
Community experience distilled.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11908393
Hidden Bibliographic Details
Varying Form of Title:Build, process, and analyze large-scale graphs with Spark
Other authors / contributors:Lee, Denny, author of introduction, etc.
ISBN:9781784398958
1784398950
9781784391805
1784391808
Notes:Includes bibliographical references and index.
Online resource; title from cover page (Safari, viewed September 28, 2015).
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.
Other form:Print version: Ramamonjison, Rindra. Apache Spark Graph Processing. Birmingham : Packt Publishing Ltd, ©2015 9781784391805

MARC

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245 1 0 |a Apache spark graph processing :  |b build, process, and analyze large-scale graphs with Spark /  |c Rindra Ramamonjison ; foreword by Denny Lee. 
246 3 0 |a Build, process, and analyze large-scale graphs with Spark 
264 1 |a Birmingham, UK :  |b Packt Publishing,  |c [2015] 
264 4 |c ©2015 
300 |a 1 online resource (1 volume) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Community experience distilled 
504 |a Includes bibliographical references and index. 
588 0 |a Online resource; title from cover page (Safari, viewed September 28, 2015). 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
520 |a 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. 
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