Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Journal articles

Discovering Patterns in Time-Varying Graphs: A Triclustering Approach

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.
Document type :
Journal articles
Complete list of metadata

Cited literature [35 references]  Display  Hide  Download
Contributor : Fabrice Rossi Connect in order to contact the contributor
Submitted on : Sunday, August 28, 2016 - 4:40:37 PM
Last modification on : Friday, May 6, 2022 - 4:50:07 PM
Long-term archiving on: : Tuesday, November 29, 2016 - 12:34:29 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution - ShareAlike 4.0 International License




Romain Guigourès, Marc Boullé, Fabrice Rossi. Discovering Patterns in Time-Varying Graphs: A Triclustering Approach. Advances in Data Analysis and Classification, Springer Verlag, 2018, 12 (3), pp.509-536. ⟨10.1007/s11634-015-0218-6⟩. ⟨hal-01356993⟩



Record views


Files downloads