Introduction to Graph Algorithms with Neo4j 4.0

Graph algorithms are used to compute metrics for graphs, nodes, or relationships. They can provide insights on relevant entities (centralities, ranking) in the graph or inherent structures such as communities (community-detection, graph-partitioning, clustering).

In this course you will learn how to use Neo4j’s Graph Data Science library to enhance your graph-based applications that require analysis.

Course outline:

Overview of graph algorithms

  • Graph analytics and algorithms

  • Graph structure

    • Propagation pathways

    • Flow and dynamics

    • Group resiliency

  • Types of graphs

  • Types of graph algorithms

Estimated time: 30 minutes

Introduction to the Graph Data Science Library

  • Neo4j support for graph analytics and visualization

  • The Neo4j Graph Data Science Library

Estimated time: 15 minutes

Environment setup

  • Prepare your environment for the hands-on exercises

  • Datasets used for this course

Estimated time: 30 minutes

Graph algorithms workflow

  • How graph algorithms work

  • Native projection

  • Cypher projection

  • Graph management

  • Using graph algorithms

  • Hands-on exercise

Estimated time: 30 minutes

Memory requirements estimation

  • Estimating memory required

  • Hands-on memory estimation

Estimated time: 10 minutes

Community detection algorithms

  • Weakly Connected Components

  • Label Propagation

  • Louvain Modularity

  • Triangle Count

  • Local Clustering Coefficient

  • Hands-on exercises

Estimated time: 60 minutes

Centrality algorithms

  • PageRank

  • Betweenness Centrality

  • Hands-on exercises

Estimated time: 45 minutes

Similarity algorithms

  • Node Similarity

  • Hands-on exercise

Estimated time: 30 minutes

Practical application of algorithms

  • Workflow overview

  • Hands-on analysis

Estimated time: 30 minutes

Additional information

  • Labs algorithms

  • Types of graphs

  • Seed parameters

  • Using Neo4j Causal Clusters

  • Common concerns

Estimated time: 10 minutes

Summary

  • Review of lessons.

  • Overview of resources for learning more and doing more with Graph Data Science in Neo4j.

  • Download certificate of completion if you have answered all questions correctly.

Estimated time: 5 minutes

Prerequisites

This course focuses on using graph algorithms with Neo4j in an applied environment to enhance functionality of an application. To be successful you should:

  • Have completed the Introduction to Neo4j 4.0 course or have equal Cypher proficiency.

  • Be familiar with using Neo4j Desktop and Neo4j Browser.

This course is published by Neo4j per this License for Use.

Resources

We have created a discussion area in our Neo4j Community Site, if you run into problems in the course and need assistance. You should register on the Community site where you can view other questions and answers for students taking our online training courses. The Neo4j Community Site is an excellent resource for answering many types of questions posed by other users of Neo4j.

Here are some resources you may use as you go through this course: