This course will introduce you to several graph algorithms in Neo4j's Graph Data Science library and explore how you can apply these to different types of graphs. You begin by building a little social network of people connected as friends. Then you will cover the steps involved in modeling friendships as undirected relationships in an in-memory graph and applying algorithms to this social network. You will use measures of centrality to identify highly connected nodes in a network. Next, you dive into community detection algorithms to find clusters of friends in a social network. From there, you will model a network as a graph with weighted edges then apply traversal algorithms on this graph, from finding shortest paths between nodes to breadth-first and depth-first traversals. Finally, you get a glimpse into the FastRP algorithm to transform nodes in your graph to vectors with a specific number of dimensions. After completing this course, you will know how to apply various graphic algorithms to extract meaningful information from a graph.

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.

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.