# facebookresearch/PyTorch-BigGraph

Generate embeddings from large-scale graph-structured data.

Repository: https://github.com/facebookresearch/PyTorch-BigGraph
Canonical: https://ross.abutalabs.com/products/pytorch-biggraph
Homepage: https://torchbiggraph.readthedocs.io/
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2024-03-03T01:42:05+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2893, "days_push": 914, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3454, forks 454 (observed 2026-08-28T04:08:05.431604+00:00)

## What it is
PyTorch-BigGraph is a distributed system for learning embeddings of very large graph-structured data, scaling to billions of entities and trillions of edges. It supports multiple knowledge graph embedding models such as TransE, RESCAL, DistMult, and ComplEx, with optional multi-machine and GPU training.

## Use cases
- generate embeddings for a large knowledge graph
- train node embeddings on billions of edges
- learn graph embeddings with TransE or ComplEx
- embed a large web interaction graph
- run distributed graph embedding training on multiple machines
- train graph embeddings on GPU

## When to choose
- your graph is too large to fit in memory on a single machine
- you need scalable knowledge graph embedding training
- you want a PyTorch-based graph embedding framework from a reputable research lab

## When to avoid
- your graph is small and fits in memory - simpler libraries like node2vec or PyTorch Geometric may suffice
- you need actively developed features - development activity is limited
- you need general-purpose GNN models rather than embedding-based link prediction

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, gpu-computing, etl
- domain: machine-learning, large-language-models, data-science
- platform: python
- tags: graph-embeddings, knowledge-graphs, distributed-training, pytorch, graph-partitioning, algorithms, linux, gpu, docker

## Member repositories
- facebookresearch/PyTorch-BigGraph (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:05.431604+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 2026-08-29T18:37:31.300783+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/PyTorch-BigGraph (fetched 2026-08-28T04:08:05.431604+00:00, sha 16d5f0c23278)
- Data as of 2026-08-30T08:39:29.467469+00:00.