# Bernoulli Embeddings for Graphs

arXiv: https://arxiv.org/abs/1803.09211 · HTML: https://mlsanity.com/paper/1803.09211/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases](https://mlsanity.com/paper/2209.05828/) (2022)

## Abstract (arXiv)

Just as semantic hashing can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying bias, continuous optimization techniques can be applied to the approximate expected loss. Embeddings optimized in this fashion consistently outperform the quantization of both spectral graph embeddings and various learned real-valued embeddings, on both ranking and pre-ranking tasks for a variety of datasets.