# alibaba/euler

A distributed graph deep learning framework.

Repository: https://github.com/alibaba/euler
Canonical: https://ross.abutalabs.com/products/euler
Language: C++
License: Apache-2.0
License Family: permissive
Topics: graph, graph-learning, network-embedding, deep-learning, graph-convolutional-networks, graph-neural-networks, graphsage, random-walk, node2vec, graph-embedding, gcn, ggnn
Last push: 2023-08-19T12:30:48+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2792, "days_push": 1110, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2897, forks 554 (observed 2026-08-28T04:07:28.774877+00:00)

## What it is
Euler is a distributed graph deep learning framework developed by Alibaba for running graph neural network algorithms on large-scale graphs. It provides a graph query language, attribute indexing, message passing interfaces, and model examples for attributed, heterogeneous, and knowledge graphs.

## Use cases
- train graph neural networks on billion-edge graphs
- compute node embeddings with node2vec or random walks
- run GraphSAGE or GCN models distributed across a cluster
- apply deep learning to knowledge graphs
- classify nodes and graphs with attributed graph data

## When to choose
- you need distributed training of GNNs on very large graphs
- you work with heterogeneous or knowledge graphs in a C++/TensorFlow stack
- you want a battle-tested industrial graph learning framework from Alibaba

## When to avoid
- you want a modern actively developed GNN library like PyTorch Geometric or DGL
- your graphs are small enough for single-machine libraries
- you need Windows or macOS support

## Facets
- artifact type: framework
- maturity: maintenance
- function: machine-learning, deep-learning, search-engine
- domain: machine-learning, deep-learning, graph-processing
- platform: cpp, python
- tags: graph-neural-networks, graph-embedding, distributed-training, network-embedding, gcn, graphsage, node2vec, knowledge-graph, algorithms, linux

## Member repositories
- alibaba/euler (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:28.774877+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 2026-08-30T07:35:13.953809+00:00, confidence not recorded.
  - readme: https://github.com/alibaba/euler (fetched 2026-08-28T04:07:28.774877+00:00, sha c50ea76a32ad)
- Data as of 2026-08-30T08:39:29.467469+00:00.