Online Course Introduction to Graph Algorithms with Neo4j 4.0 Overview of Graph Algorithms Introduction to Graph Data Science library Environment Setup Graph Algorithms Workflow Memory Requirements Estimation Community Detection Algorithms Centrality Algorithms Similarity Algorithms Practical Application of Algorithms Additional Information… Read more →

Summary

Summary

You should now be able to:

  • Describe how Neo4j supports graph algorithms for analysis and machine learning.
  • Describe the components of the Neo4j Graph Data Science library.
  • Set up your environment for running graph algorithms.
  • Describe the workflow you use to use the Graph Data Science library.
  • Determine memory requirements for your analysis.
  • Use Community Detection algorithms.
  • Use Centrality algorithms.
  • Use Similarity algorithms.
  • Perform data analysis using a set of graph algorithms.
  • Describe some best practices for using algorithms.

Resources

There are many resources available to you for learning more about Neo4j and Cypher.

Neo4j Community Site where you can ask or answer questions about Neo4j and discuss with other users:

Neo4j documentation:

Neo4j Sandboxes for experimenting with graphs:

Videos on the Neo4j YouTube channel:

Neo4j online and classroom training:

Become a Neo4j certified developer:

GitHub repository:

Neo4j events all over the world:

Graph Gists for learning more use cases for Neo4j:

Attend a Neo4j meetup:

View questions/answers raised about Neo4j:

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