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Regular decomposition of large graphs and other structures: scalability and robustness towards missing data
[article]
2017
arXiv
pre-print
A method for compression of large graphs and matrices to a block structure is further developed. Szemer\'edi'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
arXiv:1711.08629v1
fatcat:foj52rbyu5g3jflxk2i6krb5gi