## Abstract

Detecting changes in network structure is important for research into systems as diverse as financial trading networks, social networks and brain connectivity. Here we present novel Bayesian methods for detecting network structure change points. We use the stochastic block model to quantify the likelihood of a network structure and develop a score we call posterior predictive discrepancy based on sliding windows to evaluate the model fitness to the data. The parameter space for this model includes unknown latent label vectors assigning network nodes to interacting communities. Monte Carlo techniques based on Gibbs sampling are used to efficiently sample the posterior distributions over this parameter space.

Original language | English |
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Title of host publication | Monte Carlo and Quasi-Monte Carlo Methods |

Subtitle of host publication | 13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2018; Rennes; France; 1 July 2018 through 6 July 2018 |

Editors | Bruno Tuffin, Pierre L’Ecuyer |

Place of Publication | Switzerland |

Publisher | Springer-Praxis |

Pages | 107-123 |

Number of pages | 17 |

Volume | 324 |

ISBN (Electronic) | 978-3-030-43465-6 |

ISBN (Print) | 9783030434649 |

DOIs | |

Publication status | Published - 2 May 2020 |

Event | International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, 2018 - University of Rennes, Rennes, France Duration: 1 Jul 2018 → 6 Jul 2018 Conference number: 13th http://mcqmc2018.inria.fr/ |

### Publication series

Name | Springer Proceedings in Mathematics and Statistics |
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Volume | 324 |

ISSN (Print) | 2194-1009 |

ISSN (Electronic) | 2194-1017 |

### Conference

Conference | International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, 2018 |
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Abbreviated title | MCQMC 2018 |

Country | France |

City | Rennes |

Period | 1/07/18 → 6/07/18 |

Other | The MCQMC Conference is a biennial meeting on Monte Carlo and quasi-Monte Carlo methods. It usually attracts 150 to 200 mathematicians, computer scientists, statisticians and researchers in related fields. |

Internet address |

## Keywords

- Bayesian inference
- Gibbs sampling
- Networks
- Sliding window
- Stochastic block model