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

Minimal end-to-end graph workflows:

• Getting Started with DRM
• Querying and Filtering Nodes
  • Retrieving nodes by id
  • Getting node and edge attributes
  • Listing all primary keys
  • Property-based filtering
  • Multi-filter search
  • Debug snapshot
  • Cleanup

Demos

Step-by-step demonstrations covering core features:

• Delete Strategies
  • Strategy 1: CASCADE (default)
  • Strategy 2: RESTRICT
  • Strategy 3: SET NULL
  • Cascade delete through WeakNode hierarchies
  • Summary table
  • Cleanup
• Propagation Properties — Full Neo4j Demo
  • Prerequisites
  • Connection configuration
  • Step 1: Connect to Neo4j
  • Step 1.5: Clear existing data
  • Step 2: Load Game of Thrones dataset
  • Step 3: Generate YAML schema
  • Step 4: Generate Python entity classes
  • Step 5: Initialize propagation properties
  • Step 6: Transactional group creation
  • Step 7: Verify the group
  • Step 8: Sample queries
  • Step 9: Get subdocuments
  • Cleanup
• RiC-O — Graph Model Demo
  • Prerequisites
  • Connection configuration
  • Step 1: Connect to Neo4j
  • Step 1.5: Clear existing data
  • Step 2: Load real RiC-O data from the National Archives of France
  • Step 3: Initialize propagation properties
  • Step 4: Inspect the loaded graph structure
  • Step 5: Get subdocuments
  • Step 6: Query the hierarchy paths
  • Step 7: Inspect specific entities
  • Step 8: Summary statistics
  • Cleanup
• RiC-O — In-Memory Demo (NetworkX)
  • Why NetworkX?
  • Step 1: Create in-memory graph with unique persistence path
  • Step 2: Load real RiC-O data from the National Archives of France
  • Step 3: Initialize propagation properties
  • Step 4: Inspect the loaded graph structure
  • Step 5: Get subdocuments
  • Step 6: Query the hierarchy paths
  • Step 7: Inspect specific entities
  • Step 8: Summary statistics
  • Cleanup
• Vector Search with HNSW
  • Enabling a vector index
  • Querying nearest neighbors
  • Adding new nodes updates the index automatically
  • Multiple vector indexes
  • Cleanup

Interactive

Hands-on explorations with Jupyter widgets:

• WeakNode and Nested WeakNode (Interactive)
  • Overview
  • Flux bàsic (pas a pas)
  • Secció interactiva (tria operació i node)
  • Cleanup

Dataset Examples

Datasets loaded into both NetworkXGraph and Neo4jGraph:

• Karate Club dataset
  • NetworkXGraph
  • Neo4jGraph
• Bibliographic references from OpenAlex
  • NetworkXGraph
  • Neo4jGraph
• Movies dataset
  • NetworkXGraph
  • Neo4jGraph
• Game of Thrones dataset
  • NetworkXGraph
  • Neo4jGraph
• Generació de classes Python des d’ontologies RDF i esquemes YAML
  • Pipeline general
  • 1. Generació des d’una ontologia RDF/OWL
    • Pipeline complet en un pas
    • Pas a pas
      • 1. Descarregar l’ontologia
      • 2. Convertir RDF → YAML DRM
      • 3. YAML → Classes Python
      • 4. Escriure el fitxer
    • Mapeig RDF/OWL → DRM
    • Anàlisi de l’ontologia RiC-O
    • Jerarquia més profunda de RiC-O
    • Importar les classes generades
  • 2. Generació des d’un esquema YAML (datasets existents)
    • Exemple: Karate Club
    • Exemple: Bibliografia (OpenAlex)
  • 3. Generació des d’un YAML manual
  • 4. PK optional vs mandatory
  • Resum

Notes

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

• Tutorials are configured to use the drm-tool 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.