Local Algorithms for Graphs
Disordered Systems and Neural Networks
2014-09-19 v1 Data Structures and Algorithms
Abstract
We are going to analyze local algorithms over sparse random graphs. These algorithms are based on local information where local regards to a decision made by the exploration of a small neighbourhood of a certain vertex plus a believe of the structure of the whole graph and maybe added some randomness. This kind of algorithms can be a natural response to the given problem or an efficient approximation such as the Belief Propagation Algorithm.
Cite
@article{arxiv.1409.5214,
title = {Local Algorithms for Graphs},
author = {David Gamarnik and Mathieu Hemery and Samuel Hetterich},
journal= {arXiv preprint arXiv:1409.5214},
year = {2014}
}
Comments
Chapter of "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", Eds.: F. Krzakala, F. Ricci-Tersenghi, L. Zdeborova, R. Zecchina, E. W. Tramel, L. F. Cugliandolo (Oxford University Press, to appear)