Regular decomposition of large graphs and other structures: scalability and robustness towards missing data
Abstract
A method for compression of large graphs and matrices to a block structure is further developed. Szemer\'edi's regularity lemma is used as a generic motivation of the significance of stochastic block models. Another ingredient of the method is Rissanen's minimum description length principle (MDL). We continue our previous work on the subject, considering cases of missing data and scaling of algorithms to extremely large size of graphs. In this way it would be possible to find out a large scale structure of a huge graphs of certain type using only a tiny part of graph information and obtaining a compact representation of such graphs useful in computations and visualization.
Keywords
Cite
@article{arxiv.1711.08629,
title = {Regular decomposition of large graphs and other structures: scalability and robustness towards missing data},
author = {Hannu Reittu and Ilkka Norros and Fülöp Bazsó},
journal= {arXiv preprint arXiv:1711.08629},
year = {2017}
}
Comments
Accepted for publication in: Fourth International Workshop on High Performance Big Graph Data Management, Analysis, and Mining, December 11, 2017, Bosto U.S.A