@inproceedings{34123e4c060a43fd95d6c8ff05cc8148,
title = "Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks",
abstract = "The latent block model (LBM) is a exible probabilistic tool to describe interactions between node sets in bipartite networks, but it does not account for interactions of time varying intensity between nodes in unknown classes. In this paper we propose a non stationary temporal extension of the LBM that clusters simultaneously the two node sets of a bipartite network and constructs classes of time intervals on which interactions are stationary. The number of clusters as well as the membership to classes are obtained by maximizing the exact complete-data integrated likelihood relying on a greedy search approach. Experiments on simulated and real data are carried out in order to assess the proposed methodology.",
author = "Marco Corneli and Pierre Latouche and Fabrice Rossi",
year = "2015",
month = jan,
day = "1",
language = "English",
series = "23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings",
publisher = "i6doc.com publication",
pages = "225--230",
booktitle = "23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings",
note = "23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 ; Conference date: 22-04-2015 Through 24-04-2015",
}