Neo4j: Managing Graphs with the Graph Data Science Library
Neo4j | Intermediate
- 9 videos | 1h 5m 25s
- Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
Discover the key concepts covered in this courseAdd properties to an in-memory graph based on the computation of an algorithmApply the degree centrality algorithm on a graph to get the level of connectedness of each nodeUse the write function of a graph algorithm to publish the results of a computation to the underlying nodes of the databasePersist an in-memory graph to a neo4j database
Load properties from the source database of a graph when exporting it to a new databaseRemove graphs from the graph catalogExport in-memory graphs to a set of csv files containing data for nodes and relationshipsSummarize the key concepts covered in this course
IN THIS COURSE
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