  This article reviews and evaluates models of network evolution based on the
notion of structural diversity. We show that diversity is an underlying theme
of three principles of network evolution: the preferential attachment model,
connectivity and link prediction. We show that in all three cases, a dominant
trend towards shrinking diversity is apparent, both theoretically and
empirically. In previous work, many kinds of different data have been modeled
as networks: social structure, navigational structure, transport
infrastructure, communication, etc. Almost all these types of networks are not
static structures, but instead dynamic systems that change continuously. Thus,
an important question concerns the trends observable in these networks and
their interpretation in terms of existing network models. We show in this
article that most numerical network characteristics follow statistically
significant trends going either up or down, and that these trends can be
predicted by considering the notion of diversity. Our work extends previous
work observing a shrinking network diameter to measures such as the clustering
coefficient, power-law exponent and random walk return probability, and
justifies preferential attachment models and link prediction algorithms. We
evaluate our hypothesis experimentally using a diverse collection of
twenty-seven temporally evolving real-world network datasets.
