Modeling and inferring Sampling design in probabilistic random network models (2016-2019)
Timothée’s PhD was co-supervised with Pierre
Barbillon
(50%/50%), from 2016 to 2019.
PhD manucript
PhD thesis
Impact de l’échantillonnage sur l’inférence de structures dans les réseaux: application aux réseaux d’échanges de graines et à l’écologie
Tabouy, T. (2019). Impact de l’échantillonnage sur l’inférence de structures dans les réseaux: application aux réseaux d’échanges de graines et à l’écologie. Université Paris-Saclay.
<p>The stochastic block model is a
popular probabilistic model for random graphs. It is
commonly used to cluster network data by aggregating
nodes that share similar connectivity patterns into
blocks. When fitting a stochastic block model to a
partially observed network, it is important to
consider the underlying process that generates the
missing values, otherwise the inference may be
biased. This paper presents missSBM, an R package
that fits stochastic block models when the network
is partially observed, i.e., the adjacency matrix
contains not only 1s or 0s encoding the presence or
absence of edges, but also NAs encoding the missing
information between pairs of nodes. This package
implements a set of algorithms to adjust the binary
stochastic block model, possibly in the presence of
external covariates, by performing variational
inference suitable for several observation
processes. Our implementation automatically explores
different block numbers to select the most relevant
model according to the integrated classification
likelihood criterion. The integrated classification
likelihood criterion can also help determine which
observation process best fits a given
dataset. Finally, missSBM can be used to perform
imputation of missing entries in the adjacency
matrix. We illustrate the package on a network
dataset consisting of interactions between political
blogs sampled during the 2007 French presidential
election.</p>
JASA
Variational Inference for Stochastic Block Models
from Sampled Data