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Stream graphs are a very useful mode of representation for temporal networkdata, whose richness offers a wide range of possible approaches. The variousmethods aimed at generalising the classical approaches applied to staticnetworks are constantly being improved. In this paper, we describe a frameworkthat extend to stream graphs iterative weighted-rich-clubs characterisation forstatic networks proposed in [1]. The general principle is that we no longerconsider the membership of a node to one of the weighted-rich-clubs for thewhole time period, but each node is associated with a temporal profile which isthe concatenation of the successive memberships of the node to theweighted-rich-clubs that appear, disappear and change all along the period. Aclustering of these profiles gives the possibility to establish a reduced listof typical temporal profiles and so a more in-depth understanding of thetemporal structure of the network. This approach is tested on real world dataproduced by recording the interactions between different students within theirrespective schools. [1] M. Djellabi, B. Jouve, and F. Amblard. Dense and sparsevertex connectivity in networks. Journal of Complex Networks, 8(3), 2020.

Clustering of temporal nodes profiles in dynamic networks of contacts