alibaba/euler
A distributed graph deep learning framework.
Euler is a distributed graph deep learning framework designed for machine learning engineers and data scientists. It takes large, complex graph datasets, including those with varied properties and structures like knowledge graphs, and helps you apply deep learning models to them. The output is typically trained models or insights derived from the graph structure, useful for tasks like graph classification or recommendation systems.
2,900 stars. No commits in the last 6 months.
Use this if you need to build and train deep learning models on very large and complex graph-structured data.
Not ideal if you're looking for a simple graph database or a tool for basic graph analytics without deep learning.
Stars
2,900
Forks
559
Language
C++
License
Apache-2.0
Category
Last pushed
Aug 19, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/alibaba/euler"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
a-r-j/graphein
Protein Graph Library
raamana/graynet
Subject-wise networks from structural MRI, both vertex- and voxel-wise features (thickness, GM...
pykale/pykale
Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for...
dmlc/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.