  Link streams model interactions over time in a wide range of fields. Under
this model, the challenge is to mine efficiently both temporal and topological
structures. Community detection and change point detection are one of the most
powerful tools to analyze such evolving interactions. In this paper, we build
on both to detect stable community structures by identifying change points
within meaningful communities. Unlike existing dynamic community detection
algorithms, the proposed method is able to discover stable communities
efficiently at multiple temporal scales. We test the effectiveness of our
method on synthetic networks, and on high-resolution time-varying networks of
contacts drawn from real social networks.
