[ABE-L] Seminário de Probabilidade - IM-UFRJ - 23/06/25

[Maria Eulalia Vares]

Dear colleagues,



Our next seminar will be held on Monday, *June 23*, from *3:30 p.m. to 4:30
p.m*. (Rio de Janeiro local time).  The meeting will take place at room *C116-
Bloco C - CT** – Instituto de Matemática – UFRJ. *There will be no
transmission online.



Speaker: *  Marina Silva Paez (IM-UFRJ)*

Title: *Hierarchical stochastic block model for community detection in
multiplex networks*

Abstract: Multiplex networks have become increasingly more prevalent in
many fields, and have emerged as a powerful tool for modeling the
complexity of real networks. There is a critical need for developing
inference models for multiplex networks that can take into account
potential dependencies across different layers, particularly when the aim
is community detection.  We add to a limited literature by proposing a
novel and efficient Bayesian model for community detection in multiplex
networks. A key feature of our approach is the ability to model varying
communities at different network layers. In contrast, many existing models
assume the same communities for all layers. Moreover, our model
automatically picks up the necessary number of communities at each layer
(as validated by real data examples). This is appealing, since deciding the
number of communities is a challenging aspect of community detection, and
especially so in the multiplex setting, if one allows the communities to
change across layers.  Borrowing ideas from hierarchical Bayesian modeling,
we use a hierarchical Dirichlet prior to model community labels across
layers, allowing dependency in their structure.  Given the community
labels, a stochastic block model (SBM) is assumed for each layer. We
develop an efficient slice sampler for sampling the posterior distribution
of the community labels as well as the link probabilities between
communities. In doing so, we address some unique challenges posed by
coupling the complex likelihood of SBM with the hierarchical nature of the
prior on the labels.  An extensive empirical validation is performed on
simulated and real data, demonstrating the superior performance of the
model over single-layer alternatives, as well as the ability to uncover
interesting structures in real networks.

Joint work with Arash Amini  (UCLA), Marina Paez (UFRJ) e Lizhen
Lin (University of Maryland)

More complete information about the seminars can be found at

https://ppge.im.ufrj.br/seminarios-de-probabilidade/

Sincerely,

Organizers: Giulio Iacobelli and Maria Eulalia Vares














-- 
Maria Eulalia Vares
Professora Titular - Instituto de Matemática - UFRJ
Coordenadora do Programa de Pós-Graduação em Estatística
https://ppge.im.ufrj.br/
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