| --- |
| license: mit |
| task_categories: |
| - graph-ml |
| - feature-extraction |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - knowledge-graph |
| - graph-neural-networks |
| - entity-linking |
| - relationship-extraction |
| pretty_name: DropThe Entity Relationship Graph |
| --- |
| |
| # DropThe Entity Relationship Graph |
|
|
| A large-scale entity relationship dataset containing **2.9 million typed, directional connections** between 1.8 million entities spanning entertainment, media, finance, and technology. Extracted from the [DropThe](https://dropthe.org) knowledge graph. |
|
|
| ## Dataset Description |
|
|
| While most open knowledge graphs focus on encyclopedic facts (Wikidata) or narrow domains (MovieLens for ratings), this dataset captures **operational relationships** -- the connections that actually matter for building recommendation systems, search engines, and analytical tools. Every edge is typed, directional, and timestamped. |
|
|
| ### Link Types |
|
|
| | Link Type | Count | Example | |
| |-----------|-------|---------| |
| | `acted_in` | 820,000+ | Florence Pugh -> Oppenheimer | |
| | `directed` | 96,000+ | Christopher Nolan -> Oppenheimer | |
| | `produced` | 145,000+ | Emma Thomas -> Oppenheimer | |
| | `works_at` | 230,000+ | Tim Cook -> Apple | |
| | `founded` | 18,000+ | Jensen Huang -> NVIDIA | |
| | `distributed_by` | 52,000+ | Oppenheimer -> Universal Pictures | |
| | `related_to` | 1,200,000+ | Bitcoin -> Ethereum (thematic) | |
| | `traded_on` | 340,000+ | Bitcoin -> Binance | |
| | `parent_of` | 46,000+ | Alphabet -> Google | |
|
|
| ### Graph Statistics |
|
|
| - **Nodes**: 1,828,455 entities across 5 types (movies, series, people, companies, cryptocurrencies) |
| - **Edges**: 2,947,733 typed directional links |
| - **Average degree**: 3.2 connections per entity |
| - **Largest connected component**: Covers 94% of all entities |
| - **Diameter**: 8 hops (entertainment subgraph) |
|
|
| ### Data Format |
|
|
| Each edge record contains: |
|
|
| - **source_id**: Entity ID of the origin node |
| - **target_id**: Entity ID of the destination node |
| - **link_type**: Typed relationship label (see table above) |
| - **weight**: Confidence score (0.0-1.0) based on source reliability |
| - **created_at**: Timestamp of link creation |
| - **source_table**: Whether the entity comes from `entities` or `geo_entities` |
| |
| ## Intended Use |
| |
| This graph dataset is particularly suited for: |
| |
| - **Graph neural networks** -- Train GCN, GAT, or GraphSAGE models for link prediction and node classification |
| - **Knowledge graph completion** -- Predict missing edges using TransE, RotatE, or other embedding methods |
| - **Recommendation systems** -- Build multi-hop recommendation paths (actor -> movie -> director -> other movies) |
| - **Community detection** -- Identify clusters in the entertainment industry (studios, talent agencies, franchise ecosystems) |
| - **Temporal analysis** -- Study how industry relationships evolve over time using edge timestamps |
|
|
| ## Methodology |
|
|
| Links are extracted through a combination of structured API ingestion (TMDB, Wikidata, CoinGecko), automated entity resolution using the DropThe alias system (80,000+ aliases), and a bidirectional auto-linker that ensures graph consistency. The full pipeline is described on [DropThe](https://dropthe.org). |
|
|
| The enrichment process runs on a local PostgreSQL instance with validation gates that check referential integrity, duplicate detection, and type conformity before any edges are promoted to the production graph on [DropThe](https://dropthe.org/data/). |
|
|
| ## Sample |
|
|
| The included `sample_edges.csv` contains 20 representative edges across multiple relationship types, demonstrating the schema and data format. |
|
|
| ## License |
|
|
| MIT License. |
|
|
| ## Citation |
|
|
| ``` |
| @dataset{dropthe_graph_2026, |
| title={DropThe Entity Relationship Graph}, |
| author={DropThe}, |
| year={2026}, |
| url={https://dropthe.org} |
| } |
| ``` |
|
|
