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 | |
| 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 |
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Keywords
- regular decomposition
- big data
- graph analysis
- sampling
Cite this
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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
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 - IEEE Institute of Electrical and Electronic Engineers
ER -