Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns - from finding vulnerabilities and bottlenecks to 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
Table of contents
- Graph Theory and Concepts
- Graph Platforms and Processing
- Pathfinding and Graph Search Algorithms
- Centrality Algorithms
- Community Detection Algorithms
- Graph Algorithms in Practice
- Using Graph Algorithms to Enhance Machine Learning
- Additional Information and Resources
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|Author(s):||Mark Needham, Amy Hodler|
Take advantage of this course called Graph Algorithms to improve your Programming skills and better understand algorithms.
This course is adapted to your level as well as all algorithms pdf courses to better enrich your knowledge.
All you need to do is download the training document, open it and start learning algorithms for free.
This tutorial has been prepared for the beginners to help them understand basic algorithms Programming. After completing this tutorial you will find yourself at a moderate level of expertise in algorithms from where you can take yourself to next levels.
This tutorial is designed for algorithms students who are completely unaware of algorithms concepts but they have basic understanding on Programming training.
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