Regular decomposition of large graphs and other structures

Scalability and robustness towards missing data

Hannu Reittu, Ilkka Norros

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

A method for compression of large graphs and matrices to a block structure is further developed. Szemerédi'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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Big Data (Big Data)
EditorsZoran Obradovic, Ricardo Baeza-Yates, Jeremy Kepner, Raghunath Nambiar, Chonggang Wang, Masashi Toyoda, Toyotaro Suzumura, Xiaohua Hu, Alfredo Cuzzocrea, Ricardo Baeza-Yates, Jian Tang, Hui Zang, Jian-Yun Nie, Rumi Ghosh
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages3352-3357
Number of pages6
Volume2018-January
ISBN (Electronic)978-1-5386-2715-0 , 978-1-5386-2714-3
ISBN (Print)978-1-5386-2716-7
DOIs
Publication statusPublished - 12 Jan 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Big Data (Big Data) - Boston, United States
Duration: 11 Dec 201714 Dec 2017

Conference

ConferenceIEEE International Conference on Big Data (Big Data)
CountryUnited States
CityBoston
Period11/12/1714/12/17

Fingerprint

Missing Data
Scalability
Visualization
Robustness
Decomposition
Decompose
Graph in graph theory
Regularity Lemma
Block Structure
Large-scale Structure
Continue
Compression
Graph
Missing data
Scaling

Keywords

  • regular decomposition
  • big data
  • graph analysis
  • sampling

Cite this

Reittu, H., & Norros, I. (2018). Regular decomposition of large graphs and other structures: Scalability and robustness towards missing data. In Z. Obradovic, R. Baeza-Yates, J. Kepner, R. Nambiar, C. Wang, M. Toyoda, T. Suzumura, X. Hu, A. Cuzzocrea, R. Baeza-Yates, J. Tang, H. Zang, J-Y. Nie, ... R. Ghosh (Eds.), 2017 IEEE International Conference on Big Data (Big Data) (Vol. 2018-January, pp. 3352-3357). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/BigData.2017.8258320
Reittu, Hannu ; Norros, Ilkka. / Regular decomposition of large graphs and other structures : Scalability and robustness towards missing data. 2017 IEEE International Conference on Big Data (Big Data). editor / Zoran Obradovic ; Ricardo Baeza-Yates ; Jeremy Kepner ; Raghunath Nambiar ; Chonggang Wang ; Masashi Toyoda ; Toyotaro Suzumura ; Xiaohua Hu ; Alfredo Cuzzocrea ; Ricardo Baeza-Yates ; Jian Tang ; Hui Zang ; Jian-Yun Nie ; Rumi Ghosh. Vol. 2018-January Institute of Electrical and Electronic Engineers IEEE, 2018. pp. 3352-3357
@inproceedings{b66e186fb4f5404f93e3434ff42872b8,
title = "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{\'e}di'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 = "regular decomposition, big data, graph analysis, sampling",
author = "Hannu Reittu and Ilkka Norros",
year = "2018",
month = "1",
day = "12",
doi = "10.1109/BigData.2017.8258320",
language = "English",
isbn = "978-1-5386-2716-7",
volume = "2018-January",
pages = "3352--3357",
editor = "Zoran Obradovic and Ricardo Baeza-Yates and Jeremy Kepner and Raghunath Nambiar and Chonggang Wang and Masashi Toyoda and Toyotaro Suzumura and Xiaohua Hu and Alfredo Cuzzocrea and Ricardo Baeza-Yates and Jian Tang and Hui Zang and Jian-Yun Nie and Rumi Ghosh",
booktitle = "2017 IEEE International Conference on Big Data (Big Data)",
publisher = "Institute of Electrical and Electronic Engineers IEEE",
address = "United States",

}

Reittu, H & Norros, I 2018, Regular decomposition of large graphs and other structures: Scalability and robustness towards missing data. in Z Obradovic, R Baeza-Yates, J Kepner, R Nambiar, C Wang, M Toyoda, T Suzumura, X Hu, A Cuzzocrea, R Baeza-Yates, J Tang, H Zang, J-Y Nie & R Ghosh (eds), 2017 IEEE International Conference on Big Data (Big Data). vol. 2018-January, Institute of Electrical and Electronic Engineers IEEE, pp. 3352-3357, IEEE International Conference on Big Data (Big Data) , Boston, United States, 11/12/17. https://doi.org/10.1109/BigData.2017.8258320

Regular decomposition of large graphs and other structures : Scalability and robustness towards missing data. / Reittu, Hannu; Norros, Ilkka.

2017 IEEE International Conference on Big Data (Big Data). ed. / Zoran Obradovic; Ricardo Baeza-Yates; Jeremy Kepner; Raghunath Nambiar; Chonggang Wang; Masashi Toyoda; Toyotaro Suzumura; Xiaohua Hu; Alfredo Cuzzocrea; Ricardo Baeza-Yates; Jian Tang; Hui Zang; Jian-Yun Nie; Rumi Ghosh. Vol. 2018-January Institute of Electrical and Electronic Engineers IEEE, 2018. p. 3352-3357.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

TY - GEN

T1 - Regular decomposition of large graphs and other structures

T2 - Scalability and robustness towards missing data

AU - Reittu, Hannu

AU - Norros, Ilkka

PY - 2018/1/12

Y1 - 2018/1/12

N2 - A method for compression of large graphs and matrices to a block structure is further developed. Szemerédi'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.

AB - A method for compression of large graphs and matrices to a block structure is further developed. Szemerédi'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.

KW - regular decomposition

KW - big data

KW - graph analysis

KW - sampling

UR - http://www.scopus.com/inward/record.url?scp=85047804537&partnerID=8YFLogxK

U2 - 10.1109/BigData.2017.8258320

DO - 10.1109/BigData.2017.8258320

M3 - Conference article in proceedings

SN - 978-1-5386-2716-7

VL - 2018-January

SP - 3352

EP - 3357

BT - 2017 IEEE International Conference on Big Data (Big Data)

A2 - Obradovic, Zoran

A2 - Baeza-Yates, Ricardo

A2 - Kepner, Jeremy

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Toyoda, Masashi

A2 - Suzumura, Toyotaro

A2 - Hu, Xiaohua

A2 - Cuzzocrea, Alfredo

A2 - Baeza-Yates, Ricardo

A2 - Tang, Jian

A2 - Zang, Hui

A2 - Nie, Jian-Yun

A2 - Ghosh, Rumi

PB - Institute of Electrical and Electronic Engineers IEEE

ER -

Reittu H, Norros I. Regular decomposition of large graphs and other structures: Scalability and robustness towards missing data. In Obradovic Z, Baeza-Yates R, Kepner J, Nambiar R, Wang C, Toyoda M, Suzumura T, Hu X, Cuzzocrea A, Baeza-Yates R, Tang J, Zang H, Nie J-Y, Ghosh R, editors, 2017 IEEE International Conference on Big Data (Big Data). Vol. 2018-January. Institute of Electrical and Electronic Engineers IEEE. 2018. p. 3352-3357 https://doi.org/10.1109/BigData.2017.8258320