Denoising and Untangling Graphs Using Degree Priors
Abstract
This paper addresses the problem of untangling hidden graphs from a set of noisy detections of undirected edges. We present a model of the generation of the observed graph that includes degree-based structure priors on the hidden graphs. Exact inference in the model is intractable; we present an e–cient approximate inference algo- rithm to compute edge appearance posteriors. We evaluate our model and algorithm on a biological graph inference problem.
Cite
Text
Morris and Frey. "Denoising and Untangling Graphs Using Degree Priors." Neural Information Processing Systems, 2003.Markdown
[Morris and Frey. "Denoising and Untangling Graphs Using Degree Priors." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/morris2003neurips-denoising/)BibTeX
@inproceedings{morris2003neurips-denoising,
title = {{Denoising and Untangling Graphs Using Degree Priors}},
author = {Morris, Quaid D. and Frey, Brendan J.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {385-392},
url = {https://mlanthology.org/neurips/2003/morris2003neurips-denoising/}
}