python-neo4j-graph-workflow

Python Neo4j Graph Workflow

Python and Neo4j/Aura workflow for graph construction, export, and interactive HTML visualization.

This repository organizes the project into two complementary parts:

• Part I: graph generation and export using Python only
• Part II: Neo4j Aura integration inside the Python API, including graph retrieval and filtering outputs

Repository and live site

• Source repository: python-neo4j-graph-workflow
• Live GitHub Pages site: mariusoech.github.io/python-neo4j-graph-workflow

Workflow overview

The following scheme summarizes the full workflow used in this repository, from the Python-only graph construction to the Aura-integrated workflow.

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Repository structure

python-neo4j-graph-workflow/
├── README.md
├── Marius-scheme-Update.jpg
├── graph_just_python.html
├── aura_within_python.html
├── notebooks/
│   ├── Graph-categorical-final.ipynb
│   ├── Neo4j-FINAL-organized.ipynb
│   └── Filter-interface.ipynb
└── exports/
    ├── neo4j_import_python.cypher
    ├── nodes_python.csv
    ├── relationships_python.csv
    ├── cypher_fetch_used.cypher
    ├── nodes_fetched.csv
    └── rels_fetched.csv

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Part I: Python part

This first part corresponds to the Python-only workflow, where the graph is built and exported directly from Python.

Main notebook

• notebooks/Graph-categorical-final.ipynb

Generated files

• graph_just_python.html – interactive HTML graph generated from the Python workflow
• exports/neo4j_import_python.cypher – Cypher script for graph import
• exports/nodes_python.csv – exported node table
• exports/relationships_python.csv – exported relationship table

Description

In this stage, the graph is created locally in Python and exported in multiple formats. The HTML file provides an interactive visualization of the generated graph, while the CSV and Cypher files allow import into Neo4j-compatible environments.

Interactive output

Open the Python-generated graph

If the page does not load immediately, wait a few minutes for GitHub Pages deployment to finish and refresh the site.

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Part II: Aura inside Python API

This second part corresponds to the Neo4j Aura workflow inside Python, where Python connects to Aura, fetches graph elements, and generates additional interactive and filtered outputs.

Main notebooks

• notebooks/Neo4j-FINAL-organized.ipynb
• notebooks/Filter-interface.ipynb

Generated files

• aura_within_python.html – interactive HTML graph generated from the Aura-connected workflow
• exports/cypher_fetch_used.cypher – Cypher query used to fetch graph data
• exports/nodes_fetched.csv – fetched nodes exported from Aura
• exports/rels_fetched.csv – fetched relationships exported from Aura

Description

In this stage, Python interacts directly with Neo4j Aura through the API. The workflow retrieves nodes and relationships, exports them as CSV and Cypher-based outputs, and generates an additional interactive HTML visualization. The filtering notebook is used to refine and inspect the graph after data retrieval.

Interactive output

Open the Aura-generated graph

If the page does not load immediately, wait a few minutes for GitHub Pages deployment to finish and refresh the site.

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Notebooks summary

Graph-categorical-final.ipynb

Builds the graph structure in Python and exports the initial visualization and import files.

Neo4j-FINAL-organized.ipynb

Connects Python to Neo4j Aura, queries the graph, and exports retrieved graph objects and visualization files.

Filter-interface.ipynb

Provides the filtering and inspection interface for the Aura-based graph workflow.

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How to use

1. Open the notebooks in the notebooks/ folder.
2. Run the notebook corresponding to the workflow you want:
   • Graph-categorical-final.ipynb for the Python-only workflow
   • Neo4j-FINAL-organized.ipynb for the Aura-integrated workflow
   • Filter-interface.ipynb for filtering and inspection
3. Open the generated HTML files through the GitHub Pages links above, or locally in your browser.
4. Use the CSV and Cypher files in exports/ for downstream analysis or Neo4j import.

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Notes

• The figure Marius-scheme-Update.jpg provides a visual summary of the two-part workflow.
• The HTML files are included as illustrative interactive outputs.
• The CSV and Cypher files document the graph structure both before and after Aura integration.