GDS basic concepts

In this module, you will learn:

• The three-step GDSiGo to glossary for graph data science (opens in a new tab)Analysing data through the structure of its connections. Also the name of the Neo4j library that implements it. workflow: Project → Run → Write
• How to create graph projectionsiFull definition for projection (opens in a new tab)An in-memory copy of part of your database that graph algorithms run against. You choose which nodes and relationships it holds. using CypheriFull definition for Cypher (opens in a new tab)Neo4j's implementation of GQL, the ISO standard query language for graph databases. It is declarative: you describe the pattern to find, and the database decides how to find it. queries
• Different types of graph projections: monopartiteiFull definition for monopartite graph (opens in a new tab)A graph in which every node is the same kind of thing, so relationships connect like to like., bipartiteiFull definition for bipartite graph (opens in a new tab)A graph with two kinds of node, where every relationship joins one kind to the other and never two of the same kind., and multipartiteiFull definition for multipartite graph (opens in a new tab)A graph with three or more kinds of node, where relationships only ever join nodes of different kinds.
• How to manage graphs in the GDS catalog

This hands-on module uses the Movies dataset to teach practical GDS syntax and workflows.

By the end, you'll have the foundational skills needed to work with algorithms in Module 3.