@inproceedings{cornelilatoucheetal2015ars,
  TITLE = {{Modelling time evolving interactions in networks through a non stationary extension of stochastic block models}},
  AUTHOR = {Corneli, Marco and Latouche, Pierre and Rossi, Fabrice},
  abstract={{In this paper, we focus on the stochastic block model (SBM),
a probabilistic tool describing interactions between nodes of a network 
using latent clusters. The SBM assumes that the network
has a stationary structure, in which connections of 
time varying intensity are not taken into account. 
In other words, interactions between two groups are forced to have the same 
features during the whole observation time. To overcome this limitation,
we propose a partition of the whole time horizon, in which interactions are observed, 
and develop a non stationary extension of the SBM,
allowing to simultaneously cluster the nodes in a network along with 
fixed time intervals in which the interactions take place. The number 
of clusters (K for nodes, D for time intervals) as well as the class memberships are finally
obtained through maximizing the complete-data integrated likelihood 
by means of a greedy search approach. After showing  that the model 
works properly with simulated data, we focus on a real data set.  
We thus consider the three days \emph{ACM Hypertext} conference held in Turin,
June 29th - July 1st 2009. Proximity interactions between attendees 
during the first day are modelled and an interesting
clustering of the daily hours is finally obtained, with times of 
social gathering (e.g. coffee breaks) recovered by the approach. 
Applications to large networks are limited due to the computational 
complexity of the  greedy search which is dominated by
the number $K_{max}$ and $D_{max}$ of clusters used in the initialization. Therefore,
advanced clustering tools are considered to reduce the number of 
clusters expected in the data, making the greedy search applicable to 
large networks.
}},
  KEYWORDS = {Random graphs, time event clustering, stochastic block models, integrated classification likelihood},
  hal = {hal-01222325},
  BOOKTITLE = {Abstract book of the Fith International Workshop on Social Network Analysis (ARS'15)},
  ADDRESS = {Anacapri, Italy},
  YEAR = {2015},
  MONTH = 4,
  pages={21},
  language={english},
    audience = {international},
  entrysubtype={abstract-international-conference},
  proceedingstype={abstract},
  publisherlinkPending = {http://www.unisa.it/uploads/13000/abstractars15_web.pdf},
}