  The high level of dynamics in today's online social networks (OSNs) creates
new challenges for their infrastructures and providers. In particular, dynamics
involving edge creation has direct implications on strategies for resource
allocation, data partitioning and replication. Understanding network dynamics
in the context of physical time is a critical first step towards a predictive
approach towards infrastructure management in OSNs. Despite increasing efforts
to study social network dynamics, current analyses mainly focus on change over
time of static metrics computed on snapshots of social graphs. The limited
prior work models network dynamics with respect to a logical clock. In this
paper, we present results of analyzing a large timestamped dataset describing
the initial growth and evolution of Renren, the leading social network in
China. We analyze and model the burstiness of link creation process, using the
second derivative, i.e. the acceleration of the degree. This allows us to
detect bursts, and to characterize the social activity of a OSN user as one of
four phases: acceleration at the beginning of an activity burst, where link
creation rate is increasing; deceleration when burst is ending and link
creation process is slowing; cruising, when node activity is in a steady state,
and complete inactivity.
