Graph Algorithms: Practical Examples in Apache Spark & Neo4j by Amy Hodler - PDF free download eBook
Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions...
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Details of Graph Algorithms: Practical Examples in Apache Spark & Neo4j
- Exact title of the book
- Graph Algorithms: Practical Examples in Apache Spark & Neo4j
- Book author
- Amy Hodler
- Book edition
- Number of pages
- 257 pages
- File size (in PDF)
- 1028 kB
Some brief overview of book
Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You'll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value-from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.
This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j-two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark.