  Despite the wide use of networks as a versatile tool for exploring complex
social systems, little is known about how to detect and forecast abrupt changes
in social systems. In this report, we develop an early warning approach based
on network properties to detect such changes. By analysing three collaborative
social networks---one co-stardom, one patent and one scientific collaborative
network, we discover that abrupt transitions inherent in these networks can
serve as a good early warning signal, indicating, respectively, the dissolution
of the Soviet Union, the emergence of the "soft matter" research field, and the
merging of two scientific communities. We then develop a clique growth model
that explains the universal properties of these real networks and find that
they belong to a new universality class, described by the Gumbel distribution.
