A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks
Abstract
We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction.
Cite
Text
Yin et al. "A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks." Neural Information Processing Systems, 2013.Markdown
[Yin et al. "A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks." Neural Information Processing Systems, 2013.](https://mlanthology.org/neurips/2013/yin2013neurips-scalable/)BibTeX
@inproceedings{yin2013neurips-scalable,
title = {{A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks}},
author = {Yin, Junming and Ho, Qirong and Xing, Eric P},
booktitle = {Neural Information Processing Systems},
year = {2013},
pages = {422-430},
url = {https://mlanthology.org/neurips/2013/yin2013neurips-scalable/}
}