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Synthetic data generation for graph ML experiments

This codebase generates interesting graphs for ML experiments. We've currently focussed on the challenges of review prediction, given three types of nodes:

  • Product
  • Person
  • Review

A review has a score, which is the goal of each experiment to determine.

There are a range of datasets you can generate:

  • experiment_0
  • experiment_1
  • experiment_2
  • experiment_3
  • experiment_4
  • experiment_5
  • article_0 - companion to the article "An introduction to machine learning on graph databases"
  • article_1 - companion to the article "Review prediction using graph data in Neo4j and an embedding in Tensorflow"

HOWTO generate data

To generate a dataset (for example, article_0) run the following in this repository's root directory:

  1. python3 -m venv env
  2. source env/bin/activate
  3. pip3 install -r requirements.txt
  4. ./generate.py --dataset article_0

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