New GraphAcademy Course: Introduction to Graph Algorithms in Neo4j 4.0

We have added this new course to our catalog of free on-line programsIntroduction to Graph Algorithms in Neo4j 4.0.

This course is meant for knowledgeable Cypher builders and information scientists who wish to study an important greatest practices for utilizing the algorithms within the Graph Data Science™ Library for evaluation of their graphs.

This course contains hands-on workouts that provide the alternative to run the most typical set of graph algorithms utilizing the Neo4j Graph Data Science Playground, in addition to working the Cypher queries in Neo4j Browser.

If you carry out the hands-on workouts on this course, it ought to take you about 5 hours to finish.

Here are the teachings of this course:

Overview of Graph Algorithms

Learn what graph algorithms are and what buildings of a graph are used for working graph algorithms. You will study a number of the use-cases which are greatest suited to graphs and graph evaluation. You may even study concerning the kinds of graphs and the kinds of graph algorithms.

Introduction to the Graph Data Science Library

Learn how the GDS Library might help you with analytics and visualization of the information. You may even learn the way the Graph Data Science Library is organized and the way you should utilize it in follow.

Environment Setup

This lesson steps you thru arrange your system for performing the hands-on workouts of the course. You might want to:


    • Install Neo4j Desktop
    • Create a Neo4j 4.Zero database occasion
    • Install the APOC and GDSL plugins
    • Install the Graph Data Science Playground (NEuler)
    • Load the information for the workouts

Graph Algorithms Workflow

In this lesson, you’ll learn the way graph algorithms work and the way you employ them in your Cypher code. Graph administration is necessary, particularly if you find yourself performing evaluation on giant datasets. You will study some greatest practices for projecting graphs in a Graph Catalog, an in-memory set of graphs you employ to your evaluation.

Memory necessities estimation

This brief lesson teaches you check whether or not the evaluation you wish to do will match in-memory.

Community Detection Algorithms

In this lesson, you’ll study probably the most helpful graph algorithms for figuring out communities inside a graph. You will achieve expertise with:


    • Weakly Connected Components
    • Label Propagation
    • Louvain Modularity
    • Triangle Count
    • Local Clustering Coefficient

Centrality algorithms

In this lesson, you’ll study probably the most helpful graph algorithms for figuring out an important nodes within the graph. You will achieve expertise with:


    • PageRank
    • Betweenness Centrality

Similarity algorithms

In this lesson, you’ll study probably the most helpful graph algorithm (Node Similarity-Jaccard Index) for figuring out nodes which are comparable within the graph.

Practical utility of algorithms

In this lesson, you’ll study by means of a guided train carry out a number of analyses utilizing a number of graph algorithms. This is almost definitely the workflow you’ll use in your actual information.

Additional Information

In this lesson, you’ll study some necessary suggestions and greatest practices while you incorporate utilizing the GDS Library in your actual utility dataset.

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