University of Cambridge > > Isaac Newton Institute Seminar Series > Statistical clustering of temporal networks through a dynamic stochastic block model

Statistical clustering of temporal networks through a dynamic stochastic block model

Add to your list(s) Download to your calendar using vCal

  • UserCatherine Matias (CNRS (Centre national de la recherche scientifique); Universit√© Pierre et Marie Curie Paris)
  • ClockThursday 15 December 2016, 16:00-16:45
  • HouseSeminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

SNAW04 - Dynamic networks

Co-author: Vincent MIELE (CNRS / LBBE / Univ. Lyon 1)
Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach,motivated by the importance of controlling for label switching issues across the different time steps, focuses on detecting groups characterized by a stable within group connectivity behavior. We study identifiability of themodel parameters, propose an inference procedure based on a variational expectation maximization algorithm as well as a model selection criterion to select for the number of groups. We carefully discuss our initialization strategy which plays an important role in the method and compare our procedure with exi sting ones on synthetic datasets. We also illustrate our approach on dynamic contact networks, one of encounters among high school students and two others on animal interactions. An implementation of the method is available as a R package called dynsbm.

Related Links

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2022, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity