  This paper presents a statistically sound method for measuring the accuracy
with which a probabilistic model reflects the growth of a network, and a method
for optimising parameters in such a model. The technique is data-driven, and
can be used for the modeling and simulation of any kind of evolving network.
  The overall framework, a Framework for Evolving Topology Analysis (FETA), is
tested on data sets collected from the Internet AS-level topology, social
networking websites and a co-authorship network. Statistical models of the
growth of these networks are produced and tested using a likelihood-based
method. The models are then used to generate artificial topologies with the
same statistical properties as the originals. This work can be used to predict
future growth patterns for a known network, or to generate artificial models of
graph topology evolution for simulation purposes. Particular application
examples include strategic network planning, user profiling in social networks
or infrastructure deployment in managed overlay-based services.
