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Graph Data Science for Supply Chain and Logistics

This repository contains example code and demos aligning to the Neo4j Graph Data Science for Supply Chains blog series. Each subdirectory corresponds to a part in the blog series:

  1. part1 - Getting Started with Neo4j GDS and Bloom
  2. part2 - Creating Informative Metrics and Analyzing Supply Chain Performance
  3. part3 - Finding Shortest Paths, Supporting Optimization and Recommendation, and What-If Scenarios

The above parts include directions for transforming and loading data into Neo4j. If you would rather skip those steps and jump right into analysis, the ./data folder includes a pre-made copy of the Neo4j database (a.k.a. "dump file") that can be easily loaded via neo4j-admin load. The ./data folder also includes a Bloom Perspective to match the data.

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  • Jupyter Notebook 95.9%
  • Python 4.1%