  Numerous concise models such as preferential attachment have been put forward
to reveal the evolution mechanisms of real-world networks, which show that
real-world networks are usually jointly driven by a hybrid mechanism of
multiplex features instead of a single pure mechanism. To get an accurate
simulation for real networks, some researchers proposed a few hybrid models of
mixing multiple evolution mechanisms. Nevertheless, how a hybrid mechanism of
multiplex features jointly influence the network evolution is not very clear.
In this study, we introduce two methods (link prediction and likelihood
analysis) to measure multiple evolution mechanisms of complex networks. Through
tremendous experiments on artificial networks, which can be controlled to
follow multiple mechanisms with different weights, we find the method based on
likelihood analysis performs much better and gives very accurate estimations.
At last, we apply this method to some real-world networks which are from
different domains (including technology networks and social networks) and
different countries (e.g., USA and China), to see how popularity and clustering
co-evolve. We find most of them are affected by both popularity and clustering,
but with quite different weights.
