Analyze Graph Data with Python
Welcome to the GDS Python Client & Aura Graph Analytics workshop.
In this hands-on workshop, you will move from the Neo4j Browser to the Python GDS client, applying centrality, community detection, and embedding algorithms to a citation network. You’ll then learn to run pathfinding algorithms at scale using Aura Graph Analytics.
Prerequisites
Before taking this workshop, you should have:
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Completed the Graph Data Science in Practice workshop, or equivalent experience with GDS concepts
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Basic understanding of GDS projections and the Project → Run → Write workflow
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Familiarity with Python
What you will learn
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How to use the Python GDS client for graph analytics workflows
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Native projections as an alternative to Cypher projections
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Deep dives into PageRank, Betweenness Centrality, and Louvain on citation data
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Node embeddings with FastRP for machine learning features
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How to run graph analytics at scale with Aura Graph Analytics
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Pathfinding with Dijkstra’s and Yen’s K-Shortest Paths for logistics optimization
Duration
4 hours
This workshop includes
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13 lessons
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Live coding exercises
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Jupyter notebooks for Python GDS client exercises
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Real-world datasets: Cora citations, Cargo 2000 logistics
Get Support
If you find yourself stuck at any stage then our friendly community will be happy to help. You can reach out for help on the Neo4j Community Site, or head over to the Neo4j Discord server for real-time discussions.
Feedback
If you have any comments or feedback on this course you can email us on graphacademy@neo4j.com.
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