Graph Algorithms: Practical Examples in Apache Spark and Neo4j
Amy Hodler, Mark Needham
Discover how graph algorithms can help you leverage the relationships within your data to create novel inventions and develop more intelligent solutions faster. You’ll learn how graph analytics are uniquely suited to unfold complex structures and surface relationships as well as why this is leading to an explosion of graph applications. Whether you are trying to build network models or predict dynamic behavior, this book illustrates how graph algorithms can be used to uncover routes, model flows and estimate resiliency.
With an emphasis on practical uses, you’ll see how to approach implementing graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. In this book, we’ll include sample code and tips for over 15 of the most significant algorithms that cover pathfinding, centrality and community detection.
- Find out how graph analytics is different from common statistical analysis
- Understand the uses of graph algorithms and whether you have a graph analytics challenge
- Learn about various graph processing approaches and challenges
- Find out how common graph algorithms work and how they are applied
- Obtain guidance on which algorithms to use for different types of questions or data
- Explore examples with working code and sample datasets for both Spark and Neo4j