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

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The Graph Data Science (GDS) library provides data scientists and developers with the necessary tools to perform powerful analysis of their graph data. In this course, you will look at various use cases of GDS and cover some of its essential operations.
Begin with an overview of the GDS library and then dive into using the library by building in-memory graphs from the contents of your Neo4j database. Explore how to build graphs using native and Cypher projections. Next, apply a graph algorithm to your GDS graph and see how it can be used to obtain meaningful information about the nodes and relationships in your data.
After completing this course, you will have a fundamental understanding of the GDS library for Neo4j and the necessary capabilities to build your own in-memory graphs and extract significant insights.

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Objectives

Neo4j: Building Graphs with Neo4j's Graph Data Science Library discover the key concepts covered in this course list the main categories of graph algorithms and recall their use cases recognize the mechanism of creating and working with graphs in Neo4j's Graph Data Science library install the Graph Data Science library for a Neo4j DBMS create an in-memory graph using the native projection configuration for nodes and relationships use the page rank algorithm to compute a score for each node in a graph load properties from a source database to an in-memory graph apply a Graph Data Science function to read a property from a graph build a graph using the Cypher projection by setting node and relationship queries create a Cypher projection graph with properties from the source database build a sub-graph containing a subset of elements from an already existing graph summarize the key concepts covered in this course