# tomasonjo/blogs

Jupyter notebooks that support my graph data science blog posts at https://bratanic-tomaz.medium.com/

Repository: https://github.com/tomasonjo/blogs
Canonical: https://ross.abutalabs.com/products/tomasonjo-blogs
Language: Jupyter Notebook
License Family: other
Topics: graph-algorithms, graph, data-science, neo4j
Last push: 2025-12-27T11:03:22+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 59, release rhythm 35, longevity 100
- inputs: {"age_days": 2564, "days_push": 249, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1648, forks 399 (observed 2026-08-28T04:05:16.610909+00:00)

## What it is
A collection of Jupyter notebooks accompanying Tomaz Bratanic's graph data science blog posts, built around Neo4j. It serves as executable, hands-on companion material for learning graph algorithms and graph-based data exploration.

## Use cases
- learn graph data science with neo4j
- run example notebooks for graph algorithms
- follow along with blog tutorials on graph analysis
- explore knowledge graphs with python
- prototype graph queries in jupyter

## When to choose
- you want runnable notebook examples for Neo4j graph algorithms
- you're following the author's blog posts and want the accompanying code
- you're learning graph data science hands-on

## When to avoid
- you need a production-ready library or tool
- you want a maintained package with a license and stable API
- you don't use Neo4j or Python notebooks

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, database, search-engine
- domain: data-science, databases, graph-processing, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, neo4j, graph-algorithms, graph-data-science, blog-companion

## Member repositories
- tomasonjo/blogs (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.610909+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:15.724859+00:00, confidence not recorded.
  - readme: https://github.com/tomasonjo/blogs (fetched 2026-08-28T04:05:16.610909+00:00, sha 56f0651ba094)
- Data as of 2026-08-30T08:39:29.467469+00:00.