Community Recovery in Graphs with Locality
Abstract
Motivated by applications in domains such as social networks and computational biology, we study the problem of community recovery in graphs with locality. In this problem, pairwise noisy measurements of whether two nodes are in the same community or different communities come mainly or exclusively from nearby nodes rather than uniformly sampled between all node pairs, as in most existing models. We present two algorithms that run nearly linearly in the number of measurements and which achieve the information limits for exact recovery.
Cite
Text
Chen et al. "Community Recovery in Graphs with Locality." International Conference on Machine Learning, 2016.Markdown
[Chen et al. "Community Recovery in Graphs with Locality." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/chen2016icml-community/)BibTeX
@inproceedings{chen2016icml-community,
title = {{Community Recovery in Graphs with Locality}},
author = {Chen, Yuxin and Kamath, Govinda and Suh, Changho and Tse, David},
booktitle = {International Conference on Machine Learning},
year = {2016},
pages = {689-698},
volume = {48},
url = {https://mlanthology.org/icml/2016/chen2016icml-community/}
}