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A method for compression of large graphs and matrices to a block structure isfurther developed. Szemer\'edi's regularity lemma is used as a genericmotivation of the significance of stochastic block models. Another ingredientof the method is Rissanen's minimum description length principle (MDL). Wecontinue our previous work on the subject, considering cases of missing dataand scaling of algorithms to extremely large size of graphs. In this way itwould be possible to find out a large scale structure of a huge graphs ofcertain type using only a tiny part of graph information and obtaining acompact representation of such graphs useful in computations and visualization.

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