Tutorials and Examples

This section contains hands-on notebooks rendered directly in the docs.

Each dataset notebook shows how to load the same data into both NetworkXGraph and Neo4jGraph. From every notebook page you can also open the notebook in Google Colab, Kaggle, or a local Jupyter server.

Getting Started

Start here for a minimal end-to-end graph workflow:

• Getting Started with CVCDOCDB
• Querying and Filtering Nodes

Demos

Interactive explorations with widgets, vector search, and advanced features:

• RiC-O — Graph Model Demo
• RiC-O — In-Memory Demo (NetworkX)
• WeakNode and Nested WeakNode (Interactive)
• Vector Search with HNSW
• Delete Strategies
• Propagation Properties — Full Neo4j Demo

Dataset Examples

The tutorials below are grouped by dataset so you can compare how the same graph loads in the in-memory backend and in Neo4j.

Karate Club

Classic social network benchmark from Zachary’s karate club study. It has 34 members and is commonly used for community detection examples.

• Karate Club dataset

Bibliographic references

OpenAlex-based example with paper, author, and citation relationships. If network access is limited, the notebook falls back to a bundled sample so the example still runs.

• Bibliographic references from OpenAlex

Movies

Small movie-domain graph with movies, genres, and IN_GENRE relations. The loader uses a public sample API and ships with an offline fallback.

• Movies dataset

Game of Thrones

Character/house graph built from a public Thrones API. It also includes an offline fallback so the example remains usable without network access.

• Game of Thrones dataset

Generating classes from OWL/RDF

Generate Python entity classes from an RDF/OWL ontology (e.g. RiC-O). The pipeline downloads the ontology, converts it to DRM YAML, and generates Node, WeakNode, Relation, and WeakRelation subclasses.

• Generació de classes Python des d’ontologies RDF i esquemes YAML

Notes

• Notebook pages keep the original .ipynb download link in the rendered output.

• Tutorials are configured to use the cvcdocdb Jupyter kernel by default.

• Each tutorial notebook starts with an installation cell that upgrades DRM from GitHub and falls back to a local editable install when needed.

• Button links can be customized with env vars: DRM_DOCS_GITHUB_REPO, DRM_DOCS_GITHUB_REF, DRM_DOCS_LOCAL_JUPYTER_BASE, and DRM_DOCS_LOCAL_NOTEBOOK_PREFIX.