Geodesic
Poisson approximation of subgraph counts in stochastic block models and a graphon model
Coulson, Matthew1 ; Gaunt, Robert E.2 ; Reinert, Gesine2 
1 The Queen’s College, University of Oxford, High Street, Oxford, OX1 4AW, UK.
2 Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, UK.
ESAIM: Probability and Statistics, Tome 20 (2016), pp. 131-142

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Small subgraph counts can be used as summary statistics for large random graphs. We use the Stein–Chen method to derive Poisson approximations for the distribution of the number of subgraphs in the stochastic block model which are isomorphic to some fixed graph. We also obtain Poisson approximations for subgraph counts in a graphon-type generalisation of the model in which the edge probabilities are (possibly dependent) random variables supported on a subset of [0,1]. Our results apply when the fixed graph is a member of the class of strictly balanced graphs.

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Accepté le :
DOI : 10.1051/ps/2016006
Classification : 90B15, 62E17, 60F05, 05C80
Keywords: Graphon model, stochastic block model, Erdős–Rényi Mixture Model, subgraph counts, Poisson approximation, Stein–Chen method

Coulson, Matthew 1 ; Gaunt, Robert E. 2 ; Reinert, Gesine 2

1 The Queen’s College, University of Oxford, High Street, Oxford, OX1 4AW, UK.
2 Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, UK. 
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     author = {Coulson, Matthew and Gaunt, Robert E. and Reinert, Gesine},
     title = {Poisson approximation of subgraph counts in stochastic block models and a graphon model},
     journal = {ESAIM: Probability and Statistics},
     pages = {131--142},
     publisher = {EDP-Sciences},
     volume = {20},
     year = {2016},
     doi = {10.1051/ps/2016006},
     mrnumber = {3528620},
     zbl = {1353.05112},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/ps/2016006/}
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Coulson, Matthew; Gaunt, Robert E.; Reinert, Gesine. Poisson approximation of subgraph counts in stochastic block models and a graphon model. ESAIM: Probability and Statistics, Tome 20 (2016), pp. 131-142. doi: 10.1051/ps/2016006

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