  It has been shown that many complex networks shared distinctive features,
which differ in many ways from the random and the regular networks. Although
these features capture important characteristics of complex networks, their
applicability depends on the type of networks. To unravel ubiquitous
characteristics that complex networks may have in common, we adopt the
clustering coefficient as the probability measure, and present a systematic
analysis of various types of complex networks from the perspective of
statistical self-similarity. We find that the probability distribution of the
clustering coefficient is best characterized by the multifractal; moreover, the
support of the measure had a fractal dimension. These two features enable us to
describe complex networks in a unified way; at the same time, offer unforeseen
possibilities to comprehend complex networks.
