English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 20m | 284 MB
Easily reveal insights from your graph data with Neo4j
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.
This course uses real-world scenarios to introduce each concept. Using illustrative examples taken from various fields, from routing to social media to recommendation engines, this course gives you all the tools you need to use this library in your daily work.
What You Will Learn
- 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