  The rich set of interactions between individuals in the society results in
complex community structure, capturing highly connected circles of friends,
families, or professional cliques in a social network. Thanks to frequent
changes in the activity and communication patterns of individuals, the
associated social and communication network is subject to constant evolution.
Our knowledge of the mechanisms governing the underlying community dynamics is
limited, but is essential for a deeper understanding of the development and
self-optimisation of the society as a whole. We have developed a new algorithm
based on clique percolation, that allows, for the first time, to investigate
the time dependence of overlapping communities on a large scale and as such, to
uncover basic relationships characterising community evolution. Our focus is on
networks capturing the collaboration between scientists and the calls between
mobile phone users. We find that large groups persist longer if they are
capable of dynamically altering their membership, suggesting that an ability to
change the composition results in better adaptability. The behaviour of small
groups displays the opposite tendency, the condition for stability being that
their composition remains unchanged. We also show that the knowledge of the
time commitment of the members to a given community can be used for estimating
the community's lifetime. These findings offer a new view on the fundamental
differences between the dynamics of small groups and large institutions.
