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Social Network Analysis and Mining
Erschienen in: Social Network Analysis and Mining 1/2016

01.12.2016 | Original Article

Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL

verfasst von: Marco Corneli, Pierre Latouche, Fabrice Rossi

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2016

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Abstract

We develop a model in which interactions between nodes of a dynamic network are counted by non-homogeneous Poisson processes. In a block modelling perspective, nodes belong to hidden clusters (whose number is unknown) and the intensity functions of the counting processes only depend on the clusters of nodes. In order to make inference tractable, we move to discrete time by partitioning the entire time horizon in which interactions are observed in fixed-length time sub-intervals. First, we derive an exact integrated classification likelihood criterion and maximize it relying on a greedy search approach. This allows to estimate the memberships to clusters and the number of clusters simultaneously. Then, a maximum likelihood estimator is developed to estimate nonparametrically the integrated intensities. We discuss the over-fitting problems of the model and propose a regularized version solving these issues. Experiments on real and simulated data are carried out in order to assess the proposed methodology.
Vorheriger Artikel A computational approach for the experimental study of EU case law: analysis and implementation
Nächster Artikel A medical image retrieval scheme with relevance feedback through a medical social network

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Fußnoten
1
In practice, the starting time of an interaction with a duration will be considered.
 
2
The model can easily be extended to the more general framework:
$$\begin{aligned} p\left( \pi _{kgu}|a_{kgu}, b_{kgu}\right) ={\text {Gamma}}(\pi _{kgu}|a_{kgu}, b_{kgu}). \end{aligned}$$
 
3
Hereafter, the “*” notation refers to the statistics after switching/merging.
 
4
The dimension of the vector \(\varvec{\omega }\) does not change.
 
5
More informations about the way the data were collected can be found in Isella et al. (2011) or visiting the website http://​www.​sociopatterns.​org/​datasets/​hypertext-2009-dynamic-contact-network/​.
 
Literatur
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Metadaten
Titel
Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL
verfasst von
Marco Corneli
Pierre Latouche
Fabrice Rossi
Publikationsdatum
01.12.2016
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2016
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0368-3

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