Apache spark graph processing :: build, process, and analyze large-scale graphs with Spark /
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 s...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
[2015]
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781784398958 1784398950 |
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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 |
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| 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. | ||
| 546 | |a English. | ||
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| 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. 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. 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. 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. |
| ctrlnum | (OCoLC)922580769 |
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| dewey-raw | 006.3/12 |
| dewey-search | 006.3/12 |
| dewey-sort | 16.3 212 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| indexdate | 2024-11-27T13:26:48Z |
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| language | English |
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| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Ramamonjison, Rindra, author. Apache spark graph processing : build, process, and analyze large-scale graphs with Spark / Rindra Ramamonjison ; foreword by Denny Lee. Build, process, and analyze large-scale graphs with Spark Birmingham, UK : Packt Publishing, [2015] ©2015 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Community experience distilled Includes bibliographical references and index. Online resource; title from cover page (Safari, viewed September 28, 2015). 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. 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. 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. 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. 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. English. Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445 Spark (Electronic resource : Apache Software Foundation) fast Graphic methods Computer programs. http://id.loc.gov/authorities/subjects/sh85056482 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 COMPUTERS General. bisacsh Electronic data processing fast Graphic methods Computer programs fast Lee, Denny, author of introduction, etc. Print version: Ramamonjison, Rindra. Apache Spark Graph Processing. Birmingham : Packt Publishing Ltd, ©2015 9781784391805 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1063942 Volltext |
| spellingShingle | Ramamonjison, Rindra Apache spark graph processing : build, process, and analyze large-scale graphs with Spark / Community experience distilled. 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. 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. 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. 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. Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445 Spark (Electronic resource : Apache Software Foundation) fast Graphic methods Computer programs. http://id.loc.gov/authorities/subjects/sh85056482 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 COMPUTERS General. bisacsh Electronic data processing fast Graphic methods Computer programs fast |
| subject_GND | http://id.loc.gov/authorities/names/no2015027445 http://id.loc.gov/authorities/subjects/sh85056482 http://id.loc.gov/authorities/subjects/sh85042288 |
| title | Apache spark graph processing : build, process, and analyze large-scale graphs with Spark / |
| title_alt | Build, process, and analyze large-scale graphs with Spark |
| title_auth | Apache spark graph processing : build, process, and analyze large-scale graphs with Spark / |
| title_exact_search | Apache spark graph processing : build, process, and analyze large-scale graphs with Spark / |
| title_full | Apache spark graph processing : build, process, and analyze large-scale graphs with Spark / Rindra Ramamonjison ; foreword by Denny Lee. |
| title_fullStr | Apache spark graph processing : build, process, and analyze large-scale graphs with Spark / Rindra Ramamonjison ; foreword by Denny Lee. |
| title_full_unstemmed | Apache spark graph processing : build, process, and analyze large-scale graphs with Spark / Rindra Ramamonjison ; foreword by Denny Lee. |
| title_short | Apache spark graph processing : |
| title_sort | apache spark graph processing build process and analyze large scale graphs with spark |
| title_sub | build, process, and analyze large-scale graphs with Spark / |
| topic | Spark (Electronic resource : Apache Software Foundation) http://id.loc.gov/authorities/names/no2015027445 Spark (Electronic resource : Apache Software Foundation) fast Graphic methods Computer programs. http://id.loc.gov/authorities/subjects/sh85056482 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 COMPUTERS General. bisacsh Electronic data processing fast Graphic methods Computer programs fast |
| topic_facet | Spark (Electronic resource : Apache Software Foundation) Graphic methods Computer programs. Electronic data processing. COMPUTERS General. Electronic data processing Graphic methods Computer programs |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1063942 |
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