ββOpen-sourcing PyTorch-BigGraph for faster embeddings of extremely large graphs
PyTorch-BigGraphβ a tool that for faster and easier producing graph embeddings for extremely large graphs. Outputs high-quality embeddings without specialized computing resources like GPUs or huge amounts of memory.
Link: https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/
Github: https://github.com/facebookresearch/PyTorch-BigGraph
#PyTorch #Facebook #OpenSourceRelease #Embeddings #GraphLearning
PyTorch-BigGraphβ a tool that for faster and easier producing graph embeddings for extremely large graphs. Outputs high-quality embeddings without specialized computing resources like GPUs or huge amounts of memory.
Link: https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/
Github: https://github.com/facebookresearch/PyTorch-BigGraph
#PyTorch #Facebook #OpenSourceRelease #Embeddings #GraphLearning
Unsupervised community detection with modularity-based attention model
Searching for communities on graphs is hard -> no clear loss, discrete labels (usually). What we do: use soft log-liklehood approximation with tricks + GNNs to try to match classical SOTA.
Paper: https://rlgm.github.io/papers/37.pdf
#ICLR2019 #GNN #GraphLearning
Searching for communities on graphs is hard -> no clear loss, discrete labels (usually). What we do: use soft log-liklehood approximation with tricks + GNNs to try to match classical SOTA.
Paper: https://rlgm.github.io/papers/37.pdf
#ICLR2019 #GNN #GraphLearning
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