  We propose a family of statistical models for social network evolution over
time, which represents an extension of Exponential Random Graph Models (ERGMs).
Many of the methods for ERGMs are readily adapted for these models, including
maximum likelihood estimation algorithms. We discuss models of this type and
their properties, and give examples, as well as a demonstration of their use
for hypothesis testing and classification. We believe our temporal ERG models
represent a useful new framework for modeling time-evolving social networks,
and rewiring networks from other domains such as gene regulation circuitry, and
communication networks.
