Regular Decomposition of Large Graphs: Foundation of a Sampling Approach to Stochastic Block Model Fitting

• Published in: Data Science and Engineering Vol. 4; no. 1; pp. 44 - 60
• Main Authors: Reittu, Hannu, Norros, Ilkka, Räty, Tomi, Bolla, Marianna, Bazsó, Fülöp
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2019; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Algorithm Analysis and Problem Complexity; Algorithms; Analysis; Applied research; Artificial Intelligence; Big data; Chemistry and Earth Sciences; Community detection; Computer Science; Consistency; Data Mining and Knowledge Discovery; Data processing; Database Management; Decomposition; Graph theory; Graphs; Martingales; Methods; Physics; Sampling; Statistics for Engineering; Stochastic models; Systems and Data Security; Community detection; Sampling; Martingales; Consistency

We analyze the performance of regular decomposition, a method for compression of large and dense graphs. This method is inspired by Szemerédi’s regularity lemma (SRL), a generic structural result of large and dense graphs. In our method, stochastic block model (SBM) is used as a model in maximum lik...