VEstep

internal

Stochastic block model used for dynamic graphs represented by Poisson processes.
To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion.
Y. Baraud and L. Birgé (2009). <doi:10.1007/s00440-007-0126-6>.
C. Biernacki, G. Celeux and G. Govaert (2000). <doi:10.1109/34.865189>.
M. Corneli, P. Latouche and F. Rossi (2016). <doi:10.1016/j.neucom.2016.02.031>.
J.-J. Daudin, F. Picard and S. Robin (2008). <doi:10.1007/s11222-007-9046-7>.
A. P. Dempster, N. M. Laird and D. B. Rubin (1977). <http://www.jstor.org/stable/2984875>.
G. Grégoire (1993). <http://www.jstor.org/stable/4616289>.
L. Hubert and P. Arabie (1985). <doi:10.1007/BF01908075>.
M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). <doi:10.1023/A:1007665907178>.
C. Matias, T. Rebafka and F. Villers (2018). <doi:10.1093/biomet/asy016>.
C. Matias and S. Robin (2014). <doi:10.1051/proc/201447004>.
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P. Reynaud-Bouret (2006). <doi:10.3150/bj/1155735930>.

Daphnc3<a9> Giorgi

ppsbm

Clustering in Longitudinal Networks

Daphné Giorgi

Catherine Matias

Tabea Rebafka

Fanny Villers

VEstep function

<dl><dt>VE</dt>
<dd>Results of the previous VE step for iterative computation</dd>
<dt>mstep</dt>
<dd>Results of the previous mstep for iterative computation</dd>
<dt>directed</dt>
<dd>Boolean for directed (TRUE) or undirected (FALSE) case</dd>
<dt>sparse</dt>
<dd>Boolean for sparse (TRUE) or not sparse (FALSE) case</dd>
<dt>method</dt>
<dd>List of string. Can be "hist" for histogram method or "kernel" for kernel method</dd>
<dt>epsilon</dt>
<dd>Threshold for the stopping criterion of VEM and fixed point iterations</dd>
<dt>fix.iter</dt>
<dd>Maximum number of iterations of the fixed point</dd>
<dt>data</dt>
<dd>Data same of mainVEM</dd></dl>

Arguments

VE step — VEstep

<dl>

<dt>VE</dt>
<dd>Results of the previous VE step for iterative computation</dd>


<dt>mstep</dt>
<dd>Results of the previous mstep for iterative computation</dd>


<dt>directed</dt>
<dd>Boolean for directed (TRUE) or undirected (FALSE) case</dd>


<dt>sparse</dt>
<dd>Boolean for sparse (TRUE) or not sparse (FALSE) case</dd>


<dt>method</dt>
<dd>List of string. Can be "hist" for histogram method or "kernel" for kernel method</dd>


<dt>epsilon</dt>
<dd>Threshold for the stopping criterion of VEM and fixed point iterations</dd>


<dt>fix.iter</dt>
<dd>Maximum number of iterations of the fixed point</dd>


<dt>data</dt>
<dd>Data same of mainVEM</dd>

</dl>

VE step

VEstep: VE step

Description

Usage

Arguments