[ABE-L] COLMEA - Colóquio Interinstitucional - 17 de maio - no IMPA

[Maria Eulalia Vares]

Prezados colegas,

Nosso próximo encontro do COLMEA será no dia 17 de maio (quarta-feira), no
IMPA.  Na ocasião teremos a seguinte programação:

*14:15h - Andressa Cerqueira (UFSCar)*

*Community detection in weighted networks *

*15:50h - Gabor Lugosi (ICREA - U. Pompeu Fabra)*

*Problems in network archaeology: root finding and broadcasting*

Todos são muito bem-vindos.

*Local do evento: Auditório 1 - IMPA. Estrada Dona Castorina 110. Jardim
Botânico. Rio de Janeiro.*

Mais informações sobre o COLMEA podem ser encontradas através da homepage
http://www.im.ufrj.br/~coloquiomea/

Agradecemos se puder divulgar. Em anexo o cartaz de divulgação. No final da
mensagem  inserimos os resumos das palestras.

Atenciosamente,

O comitê organizador:

Americo Cunha (UERJ)

Evaldo M. F. Curado (CBPF)

João Batista M. Pereira (UFRJ)

Leandro P. R. Pimentel (UFRJ)

Maria Eulalia Vares (UFRJ)

Nuno Crokidakis (UFF)

Roberto I. Oliveira (IMPA)

Simon Griffiths (PUC-Rio)

Yuri F. Saporito (FGV EMAp)





*Community detection in weighted networks*
Andressa Cerqueira (UFSCar)

Network models have received increasing attention from the statistical
community, in particular in the context of analyzing and describing the
interactions of complex random systems. In this context, community
structures can be observed in many networks where the nodes are clustered
in groups with the same connection patterns. In this talk, we address the
community detection problem for weighted networks in the case where,
conditionally on the node labels, the edge weights are drawn independently
from a Gaussian random variable with mean and variance depending on the
community labels of the edge endpoints. We will present a fast and
tractable EM algorithm to recover the community labels that achieves the
optimal error rate.



*Problems in network archaeology: root finding and broadcasting*
Gábor Lugosi (ICREA - U. Pompeu Fabra)

Large networks are often naturally modeled by random processes in which
nodes of the network are added sequentially, according to some stochastic
rule. Uniform and preferential attachment trees are among the simplest
examples of such dynamically growing networks. The statistical problems we
address in this talk regard discovering the past of the network when a
present-day snapshot is observed. We present results that show that, even
in gigantic networks, a lot of information is preserved from the very early
days. In particular, we discuss the problem of finding the root and the
broadcasting problem.




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