Graph Algorithms
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
- Introduction
- 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
| Pages : | 257 |
| Size : | 10.8 MB |
| File type : | |
| Downloads: | 100 |
| Created: | 2022-02-02 |
| License: | CC BY |
| Author(s): | Mark Needham, Amy Hodler |
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Others algorithms Tutorials
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