Abstract : This paper introduces a novel technique to track structures in time varying graphs. The method uses a maximum a posteriori approach for adjusting a three-dimensional co-clustering of the source vertices, the destination vertices and the time, to the data under study, in a way that does not require any hyper-parameter tuning. The three dimensions are simultaneously segmented in order to build clusters of source vertices, destination vertices and time segments where the edge distributions across clusters of vertices follow the same evolution over the time segments. The main novelty of this approach lies in that the time segments are directly inferred from the evolution of the edge distribution between the vertices, thus not requiring the user to make any a priori quantization. Experiments conducted on artificial data illustrate the good behavior of the technique, and a study of a real-life data set shows the potential of the proposed approach for exploratory data analysis.
Type de document :
Article dans une revue
Advances in Data Analysis and Classification, Springer Verlag, 2015, Online First, 〈10.1007/s11634-015-0218-6〉
https://hal.archives-ouvertes.fr/hal-01356993
Contributeur : Fabrice Rossi
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Soumis le : dimanche 28 août 2016 - 16:40:37
Dernière modification le : lundi 27 novembre 2017 - 14:14:02
Document(s) archivé(s) le : mardi 29 novembre 2016 - 12:34:29
Romain Guigourès, Marc Boullé, Fabrice Rossi. Discovering Patterns in Time-Varying Graphs: A Triclustering Approach. Advances in Data Analysis and Classification, Springer Verlag, 2015, Online First, 〈10.1007/s11634-015-0218-6〉. 〈hal-01356993〉
