Advances in Data Analysis and Classification

, Volume 12, Issue 3, pp 509–536 | Cite as

Discovering patterns in time-varying graphs: a triclustering approach

  • Romain Guigourès
  • Marc Boullé
  • Fabrice RossiEmail author
Regular Article


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.


Co-clustering Time-varying graph Graph mining Model selection 

Mathematics Subject Classification

62P25 62H30 62G07 62B10 62F15 



The authors thank the anonymous reviewers and the associate editor for their valuable comments that helped improving this paper.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Romain Guigourès
    • 1
  • Marc Boullé
    • 1
  • Fabrice Rossi
    • 2
    Email author
  1. 1.Orange LabsLannionFrance
  2. 2.SAMM EA 45 43Université Paris 1ParisFrance

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