  Community detection is an important tool for analyzing the social graph of
mobile phone users. The problem of finding communities in static graphs has
been widely studied. However, since mobile social networks evolve over time,
static graph algorithms are not sufficient. To be useful in practice (e.g. when
used by a telecom analyst), the stability of the partitions becomes critical.
We tackle this particular use case in this paper: tracking evolution of
communities in dynamic scenarios with focus on stability. We propose two
modifications to a widely used static community detection algorithm: we
introduce fixed nodes and preferential attachment to pre-existing communities.
We then describe experiments to study the stability and quality of the
resulting partitions on real-world social networks, represented by monthly call
graphs for millions of subscribers.
