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Graph algorithms : practical examples in Apache Spark and Neo4j /
Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used for building...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Beijing :
O'Reilly,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Graph algorithms : |b practical examples in Apache Spark and Neo4j / |c Mark Needham and Amy E. Hodler. |
| 264 | 1 | |a Beijing : |b O'Reilly, |c 2019. | |
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| 505 | 0 | |a Cover; Copyright; Table of Contents; Preface; What's in This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Online Learning; How to Contact Us; Acknowledgments; Foreword; Chapter 1. Introduction; What Are Graphs?; What Are Graph Analytics and Algorithms?; Graph Processing, Databases, Queries, and Algorithms; OLTP and OLAP; Why Should We Care About Graph Algorithms?; Graph Analytics Use Cases; Conclusion; Chapter 2. Graph Theory and Concepts; Terminology; Graph Types and Structures; Random, Small-World, Scale-Free Structures; Flavors of Graphs | |
| 505 | 8 | |a Connected Versus Disconnected GraphsUnweighted Graphs Versus Weighted Graphs; Undirected Graphs Versus Directed Graphs; Acyclic Graphs Versus Cyclic Graphs; Sparse Graphs Versus Dense Graphs; Monopartite, Bipartite, and k-Partite Graphs; Types of Graph Algorithms; Pathfinding; Centrality; Community Detection; Summary; Chapter 3. Graph Platforms and Processing; Graph Platform and Processing Considerations; Platform Considerations; Processing Considerations; Representative Platforms; Selecting Our Platform; Apache Spark; Neo4j Graph Platform; Summary | |
| 505 | 8 | |a Chapter 4. Pathfinding and Graph Search AlgorithmsExample Data: The Transport Graph; Importing the Data into Apache Spark; Importing the Data into Neo4j; Breadth First Search; Breadth First Search with Apache Spark; Depth First Search; Shortest Path; When Should I Use Shortest Path?; Shortest Path with Neo4j; Shortest Path (Weighted) with Neo4j; Shortest Path (Weighted) with Apache Spark; Shortest Path Variation: A*; Shortest Path Variation: Yen's k-Shortest Paths; All Pairs Shortest Path; A Closer Look at All Pairs Shortest Path; When Should I Use All Pairs Shortest Path? | |
| 505 | 8 | |a All Pairs Shortest Path with Apache SparkAll Pairs Shortest Path with Neo4j; Single Source Shortest Path; When Should I Use Single Source Shortest Path?; Single Source Shortest Path with Apache Spark; Single Source Shortest Path with Neo4j; Minimum Spanning Tree; When Should I Use Minimum Spanning Tree?; Minimum Spanning Tree with Neo4j; Random Walk; When Should I Use Random Walk?; Random Walk with Neo4j; Summary; Chapter 5. Centrality Algorithms; Example Graph Data: The Social Graph; Importing the Data into Apache Spark; Importing the Data into Neo4j; Degree Centrality; Reach | |
| 505 | 8 | |a When Should I Use Degree Centrality?Degree Centrality with Apache Spark; Closeness Centrality; When Should I Use Closeness Centrality?; Closeness Centrality with Apache Spark; Closeness Centrality with Neo4j; Closeness Centrality Variation: Wasserman and Faust; Closeness Centrality Variation: Harmonic Centrality; Betweenness Centrality; When Should I Use Betweenness Centrality?; Betweenness Centrality with Neo4j; Betweenness Centrality Variation: Randomized-Approximate Brandes; PageRank; Influence; The PageRank Formula; Iteration, Random Surfers, and Rank Sinks; When Should I Use PageRank? | |
| 520 | |a Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns-from finding vulnerabilities and bottlenecksto detecting communities and improving machine learning predictions. You'll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today's data Understand how popular graph algorithms work and how they're applied Use sample code and tips from more than 20 graph algorithm examples Learn which algorithms to use for different types of questions Explore examples with working code and sample datasets for Spark and Neo4j Create an ML workflow for link prediction by combining Neo4j and Spark. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
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| 700 | 1 | |a Hodler, Amy E., |e author. | |
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