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 |
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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 | |
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) |
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Country/Territory | United States |
City | Boston |
Period | 11/12/17 → 14/12/17 |
Keywords
- regular decomposition
- big data
- graph analysis
- sampling