Book Image

Apache Spark Graph Processing

Book Image

Apache Spark Graph Processing

Overview of this book

Table of Contents (16 chapters)
Apache Spark Graph Processing
Credits
Foreword
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Chapter 3. Graph Analysis and Visualization

In this chapter, we will learn how to analyze the characteristics of graphs using visualization tools and graph algorithms. For example, we will use some of the algorithms available in GraphX to see how connected a graph is. In addition, we will compute metrics that are commonly used, such as triangle counting and clustering coefficients. Furthermore, we will learn through a concrete example how the PageRank algorithm can be used to rank the importance of the nodes in a network. Along the way, we will introduce new RDD operations that will prove out to be useful here and in later chapters. Finally, this chapter offers practical tips on building Spark applications that rely on the third-party libraries. After doing the activities in this chapter, you will have learned the tools and concepts to:

  • Visualize large-scale graph data

  • Compute the connected components of a network

  • Use the PageRank algorithm to rank the node importance in networks

  • Build Spark...