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

Hannu Reittu; Ilkka Norros

doi:10.1109/BigData.2017.8258320

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

BA1509 Autonomous systems connectivity

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-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 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
Number of pages	6
Volume	2018-January
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 - 12 Jan 2018
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	United States
City	Boston
Period	11/12/17 → 14/12/17

Keywords

regular decomposition
big data
graph analysis
sampling

Access to Document

10.1109/BigData.2017.8258320

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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). IEEE Institute of Electrical and Electronic Engineers. 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 IEEE Institute of Electrical and Electronic Engineers, 2018. pp. 3352-3357

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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.",

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booktitle = "2017 IEEE International Conference on Big Data (Big Data)",

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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, IEEE Institute of Electrical and Electronic Engineers, 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 IEEE Institute of Electrical and Electronic Engineers, 2018. p. 3352-3357.

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

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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.

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VL - 2018-January

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

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A2 - Baeza-Yates, Ricardo

A2 - Kepner, Jeremy

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Toyoda, Masashi

A2 - Suzumura, Toyotaro

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A2 - Cuzzocrea, Alfredo

A2 - Baeza-Yates, Ricardo

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PB - IEEE Institute of Electrical and Electronic Engineers

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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. IEEE Institute of Electrical and Electronic Engineers. 2018. p. 3352-3357 https://doi.org/10.1109/BigData.2017.8258320