Clustering of temporal nodes profiles in dynamic networks of contacts

Mehdi Djellabi , Bertrand Jouve (1)

1 LISST - Laboratoire Interdisciplinaire Solidarités, Sociétés, Territoires

Stream graphs are a very useful mode of representation for temporal network data, whose richness offers a wide range of possible approaches. The various methods aimed at generalising the classical approaches applied to static networks are constantly being improved. In this paper, we describe a framework that extend to stream graphs iterative weighted-rich-clubs characterisation for static networks proposed in [1]. The general principle is that we no longer consider the membership of a node to one of the weighted-rich-clubs for the whole time period, but each node is associated with a temporal profile which is the concatenation of the successive memberships of the node to the weighted-rich-clubs that appear, disappear and change all along the period. A clustering of these profiles gives the possibility to establish a reduced list of typical temporal profiles and so a more in-depth understanding of the temporal structure of the network. This approach is tested on real world data produced by recording the interactions between different students within their respective schools. [1] M. Djellabi, B. Jouve, and F. Amblard. Dense and sparse vertex connectivity in networks. Journal of Complex Networks, 8(3), 2020.

Domaines: Mathématique discrète [cs.DM]; Réseaux sociaux et d'information [cs.SI]

Mehdi Djellabi, Bertrand Jouve. Clustering of temporal nodes profiles in dynamic networks of contacts. FRCCS21 :French Regional Conference on Complex Systems, May 2021, Dijon, France. ⟨hal-03198407⟩