Essential projection techniques
You've learned how to create basic Full 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. and run algorithms with various configurations.
Now it's time to add some more advanced projection configurations to your arsenal.
In this module, you'll learn:
- How to aggregate multiple Full definition for relationship (opens in a new tab)A named, directed connection between two nodes. Every relationship has a type, a start node and an end node. into weighted connections
- When to use Full definition for relationship aggregation (opens in a new tab)Collapsing the parallel relationships between two nodes into one during projection, usually carrying their count or sum as its weight. for performance and insight
- How to design projections based on analytical questions
- How to match projection structure to algorithm requirements
- How to think strategically about projection modeling
This module brings together everything you've learned about projections, combining them with your algorithm knowledge to solve complex analytical problems.
By the end of this module, you'll be able to independently design and implement Go 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. solutions from analytical question through to projection, algorithm selection, and result interpretation.
You'll have all the foundational skills needed to apply GDS to real-world problems in the Applied Algorithms in GDS course.