  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.
