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Compound Poisson approximation of subgraph counts in stochastic block models with multiple edges

  • Matthew Coulson (a1), Robert E. Gaunt (a2) and Gesine Reinert (a3)


We use the Stein‒Chen method to obtain compound Poisson approximations for the distribution of the number of subgraphs in a generalised stochastic block model which are isomorphic to some fixed graph. This model generalises the classical stochastic block model to allow for the possibility of multiple edges between vertices. We treat the case that the fixed graph is a simple graph and that it has multiple edges. The former results apply when the fixed graph is a member of the class of strictly balanced graphs and the latter results apply to a suitable generalisation of this class to graphs with multiple edges. We also consider a further generalisation of the model to pseudo-graphs, which may include self-loops as well as multiple edges, and establish a parameter regime in the multiple edge stochastic block model in which Poisson approximations are valid. The results are applied to obtain Poisson and compound Poisson approximations (in different regimes) for subgraph counts in the Poisson stochastic block model and degree corrected stochastic block model of Karrer and Newman (2011).


Corresponding author

* Postal address: School of Mathematics, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
** Postal address: School of Mathematics, The University of Manchester, Manchester M13 9PL, UK. Email address:
*** Postal address: Department of Statistics, University of Oxford, 24‒29 St Giles', Oxford OX1 3LB, UK


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Advances in Applied Probability
  • ISSN: 0001-8678
  • EISSN: 1475-6064
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