Regular decomposition of large graphs and other structures: Scalability and robustness towards missing data

Hannu Reittu; Ilkka Norros; Fülöp Bazsó

doi:10.1109/BigData.2017.8258320

Regular decomposition of large graphs and other structures: Scalability and robustness towards missing data

Hannu Reittu, Ilkka Norros, Fülöp Bazsó

Hungarian Academy of Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

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 language	English
Title of host publication	2017 IEEE International Conference on Big Data (Big Data)
Editors	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
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	3352-3357
ISBN (Electronic)	978-1-5386-2715-0 , 978-1-5386-2714-3
ISBN (Print)	978-1-5386-2716-7
DOIs	https://doi.org/10.1109/BigData.2017.8258320
Publication status	Published - 2017
MoE publication type	A4 Article in a conference publication
Event	IEEE International Conference on Big Data (Big Data) - Boston, United States Duration: 11 Dec 2017 → 14 Dec 2017

Conference

Conference	IEEE International Conference on Big Data (Big Data)
Country/Territory	United States
City	Boston
Period	11/12/17 → 14/12/17

Keywords

regular decomposition
big data
graph analysis
sampling

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10.1109/BigData.2017.8258320

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Reittu, H., Norros, I., & Bazsó, F. (2017). 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) (pp. 3352-3357). IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/BigData.2017.8258320

Reittu, Hannu ; Norros, Ilkka ; Bazsó, Fülöp. / 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. IEEE Institute of Electrical and Electronic Engineers, 2017. pp. 3352-3357

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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 and F{\"u}l{\"o}p Bazs{\'o}",

year = "2017",

doi = "10.1109/BigData.2017.8258320",

language = "English",

isbn = "978-1-5386-2716-7 ",

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 = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "IEEE International Conference on Big Data (Big Data) ; Conference date: 11-12-2017 Through 14-12-2017",

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Reittu, H, Norros, I & Bazsó, F 2017, 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). IEEE Institute of Electrical and Electronic Engineers, pp. 3352-3357, IEEE International Conference on Big Data (Big Data) , Boston, Massachusetts, 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; Bazsó, Fülöp.
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. IEEE Institute of Electrical and Electronic Engineers, 2017. p. 3352-3357.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Regular decomposition of large graphs and other structures

T2 - IEEE International Conference on Big Data (Big Data)

AU - Reittu, Hannu

AU - Norros, Ilkka

AU - Bazsó, Fülöp

PY - 2017

Y1 - 2017

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

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U2 - 10.1109/BigData.2017.8258320

DO - 10.1109/BigData.2017.8258320

M3 - Conference article in proceedings

SN - 978-1-5386-2716-7

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 - IEEE Institute of Electrical and Electronic Engineers

Y2 - 11 December 2017 through 14 December 2017

ER -

Reittu H, Norros I, Bazsó F. 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 JY, Ghosh R, editors, 2017 IEEE International Conference on Big Data (Big Data). IEEE Institute of Electrical and Electronic Engineers. 2017. p. 3352-3357 doi: 10.1109/BigData.2017.8258320

Regular decomposition of large graphs and other structures: Scalability and robustness towards missing data

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