How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Generate embeddings from large-scale graph-structured data.
The main features of facebookresearch/pytorch-biggraph are: Embedding Algorithms, Graph Machine Learning, Geometric Deep Learning: Graph & Irregular Structures.
Open-source alternatives to facebookresearch/pytorch-biggraph include: benedekrozemberczki/seal-ci — A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019). benedekrozemberczki/sine — A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018). benedekrozemberczki/attentionwalk — A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018). benedekrozemberczki/karateclub — Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020). benedekrozemberczki/sgcn — A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018). benedekrozemberczki/splitter — A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).