This is the code repository for Exploring Graph Algorithms with Neo4j [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Neo4j is an open-source, highly scalable and transactional graph database well suited to connected data. It is the world's leading graph database management system which is designed for optimizing fast management, storage, and traversal of nodes and relationships.
You can use it for artificial intelligence, fraud detection, graph-based search, network ops & security, and many other use cases. Graph algorithms which are included in Neo4j’s growing and open library.In this course, you will cover the important graph algorithms which are used in Neo4j’s graph analytics platform. This will be an engaging and practical course, where you will explore various high-performance graph algorithms that help reveal hidden patterns and structures in your connected data. You will learn how to master your skills to use the algorithms efficiently to understand, model and predict complicated dynamics. You will also be able to develop and deploy graph-based solutions faster and have streamlined workflows as well as solve real-world problems. With the help of this course, you will learn how to make your work easier by selecting the right algorithm based on your requirement, understand its workings and implement it.
By the end of the course, you will be familiar and confident with the graph analytics with Neo4j to deal with a broad range of problems and learn to use its quick insights to wield powerful results
- Use Neo4j graph algorithms library with your real data
- Solve routing problems by finding paths inside a connected graph
- Find the most influential nodes in your database
- Create group of nodes sharing common properties, aka communities
- Build a recommendation system using similarity measurement between nodes
- Predict whether a relationship between two nodes exists
To fully benefit from the coverage included in this course, you will need:
To fully benefit from the coverage included in this course, you will need:
Graph models with Neo4j
Basis of cypher query language
(Optional) Experience with data science and machine learning
This course has the following software requirements:
SETUP AND INSTALLATION
Minimum Hardware Requirements Neo4j desktop ⇒ Recommended system requirements: MacOS 10.10 (Yosemite)+, Windows 7+, Ubuntu 12.04+, Fedora 21, Debian 8.
For successful completion of this course, students will require the computer systems with at least the following:
OS: Windows 10, Mac OS X 10.12, Ubuntu 18.04, Fedora or Debian 9 (TBD)
Processor: 64 bit processor
Memory: 8GB
Storage: 20 GB (TBD, depending on the dataset size)
Recommended Hardware Requirements For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration: OS:
Windows 10, Mac OS X 10.12, Ubuntu 18.04, Fedora or Debian 9 (TBD)
Processor: 64 bit processor
Memory: 16GB
Storage: 20GB Software Requirements
neo4j database (version 3.5.3+) neo4j-desktop-offline (version 1.1.15+)
https://neo4j.com/download-center/ https://neo4j.com/download/
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