Graph Data Science with Neo4j by Estelle Scifo

Author:Estelle Scifo
Language: eng
Format: epub
Publisher: Pack Publishing Pvt Ltd
Published: 2023-01-30T00:00:00+00:00

These are just two examples to give you some hints about why communities are important. They not only help you understand the data but they can also be used to make decisions. We will see in the following chapters that adding community as a feature in an ML model can drastically increase model performance.

Summary

In this chapter, you have learned the basic principles of the Neo4j GDS library 2.x. You have been able to create projected graphs, configuring included nodes, relationships, and properties with native graph projection. You have also learned how to generate properties or relationships on the fly using Cypher projections. In the second section, you have run your first GDS algorithm—the degree algorithm—and got familiar with the stream, write, and mutate algorithm modes. You have also been made aware of the algorithm configuration, especially regarding relationship orientation.

Once GDS had no more secrets to you, we started using other types of algorithms—namely, community detection algorithms. We studied a few of them and learned about their differences and what they can teach us about our graph.

In the next chapter, we will learn how to use another powerful tool of the Neo4j universe: Neo4j Bloom, yet another graph application. Bloom is designed to make graph visualization accessible to everyone, and, as we all know, with great visualization comes great insights.

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