Aspire     Graph Analytics     Graph Analytics Track 3: Graph Data Science with Neo4j

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This course will teach you to use operations that modify in-memory graphs built with the Graph Data Science (GDS) library, update the properties of the underlying database, and export their contents to a database or file.
You begin with a mutate operation to add new properties to an in-memory graph. You will learn how you can save the results of graph algorithms in such graphs for later reference. Next, you explore the write operation to update the underlying database with the results of graph algorithms on in-memory graphs. You will then move on to exporting your graph to a persistent store.
Finally, you cover the different ways you can remove GDS graphs from the graph catalog. While doing so, you will explore the degree centrality calculation, which measures how well-connected nodes are in a network.
After completing this course, you will have a fundamental understanding of how to administer in-memory graphs using the Graph Data Science library in Neo4j.

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Objectives

Neo4j: Managing Graphs with the Graph Data Science Library discover the key concepts covered in this course add properties to an in-memory graph based on the computation of an algorithm apply the degree centrality algorithm on a graph to get the level of connectedness of each node use the write function of a graph algorithm to publish the results of a computation to the underlying nodes of the database persist an in-memory graph to a Neo4j database load properties from the source database of a graph when exporting it to a new database remove graphs from the graph catalog export in-memory graphs to a set of CSV files containing data for nodes and relationships summarize the key concepts covered in this course