目錄(62章)
倒序
- coverpage
- Apache Spark Graph Processing
- Credits
- Foreword
- About the Author
- About the Reviewer
- www.PacktPub.com
- Support files eBooks discount offers and more
- Preface
- Distinctive features
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Chapter 1. Getting Started with Spark and GraphX
- Downloading and installing Spark 1.4.1
- Experimenting with the Spark shell
- Getting started with GraphX
- Summary
- Chapter 2. Building and Exploring Graphs
- Network datasets
- Graph builders
- Building graphs
- Computing the degrees of the network nodes
- Summary
- Chapter 3. Graph Analysis and Visualization
- Network datasets
- The graph visualization
- The analysis of network connectedness
- The network centrality and PageRank
- Scala Build Tool revisited
- Summary
- Chapter 4. Transforming and Shaping Up Graphs to Your Needs
- Transforming the vertex and edge attributes
- Modifying graph structures
- Joining graph datasets
- Data operations on VertexRDD and EdgeRDD
- Summary
- Chapter 5. Creating Custom Graph Aggregation Operators
- NCAA College Basketball datasets
- The aggregateMessages operator
- Joining average stats into a graph
- Performance optimization
- The MapReduceTriplets operator
- Summary
- Chapter 6. Iterative Graph-Parallel Processing with Pregel
- The Pregel computational model
- The Pregel API in GraphX
- Community detection through label propagation
- The Pregel implementation of PageRank
- Summary
- Chapter 7. Learning Graph Structures
- Community clustering in graphs
- Applications – music fan community detection
- Summary
- Appendix A. References
- Chapter 2 Building and Exploring Graphs
- Chapter 3 Graph Analysis and Visualization
- Chapter 7 Learning Graph Structures
- Index 更新時間:2021-07-16 20:04:01
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