STOCHASTIC BLOCK MODEL AND CLASSIFICATION
DOI:
https://doi.org/10.24867/11BE16JankovKeywords:
Graphs, Stochastic block model, classificationAbstract
An analysis of the stochastic block model for graphs and how we can use the landing probabilities for classification. In this work we grouped the centroids of different graphs generated with the stochasitc block model and concluded that their landing probabilities converge to a single point.
References
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[2] Emmanuel Abbe, Afonso S Bandeira, and Georgina Hall, “ Exact recovery in the stohastic block model”, IEEE Transactions on, 62(1):471–487, 2016.
[3] http://vtsns.edu.rs/wp-content/uploads/2019/01/Prim-metode-modelovanja-rizika-eksp_Skripta-I-deo.pdf (pristupljeno u septembru 2020.)
[4] James P Bagrow, “ Evaluating local community methods in networks”, Journal of Statistical
Mechanics: Theory and Experiment, 2008(05):P05001, 2008.
[5] A.E. Bryson, Y.C. Ho, “Applied Optimal Control”, New York, Wiley, 1975.
[6] Emmanuel Abbe, “ Community Detection and Stochastic Block Models: Recent Developments”, Journal of Machine Learning Research 18 (2018) 1-86.
[7] Paul Erdos and Alfred Renyi, “ On random graphs”, Publ. Math. Debrecen., 6:290–297, 1959
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Published
2020-12-25
Issue
Section
Electrotechnical and Computer Engineering
