Uploaded November 2021 | Updated September 2026, 2 weeks ago
This video provides an overview of Graph Embeddings and how PyTorch-BigGraph enables learning graph embeddings for very large graphs. The key challenge with this is you would need a lot of memory to store the vectors for each node in a large graph. To solve this, BigGraph uses novel partitioning, distributed execution, and negative sampling algorithms. I hope this is a decent introduction to graph embeddings and PyTorch-BigGraph, really excited about the upcoming release of the Wikidata Weaviate web demo!
PyTorch-BigGraph (blog post) - ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs
PyTorch-BigGraph (paper) - arxiv.org/abs/1903.12287
Paperswithdata (graph filter) - paperswithcode.com/datasets?mod=graphs&page=1
This video provides an overview of Graph Embeddings and how PyTorch-BigGraph enables learning graph embeddings for very large graphs. The key challenge with this is you would need a lot of memory to store the vectors for each node in a large graph. To solve this, BigGraph uses novel partitioning, distributed execution, and negative sampling algorithms. I hope this is a decent introduction to graph embeddings and PyTorch-BigGraph, really excited about the upcoming release of the Wikidata Weaviate web demo!
PyTorch-BigGraph (blog post) - ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs
PyTorch-BigGraph (paper) - arxiv.org/abs/1903.12287
Paperswithdata (graph filter) - paperswithcode.com/datasets?mod=graphs&page=1
