  Researchers have typically concentrated on analyzing what happens internally
in a complex network and using this to distinguish between nodes. However,
there has been less effort towards comparing between different networks. In
this paper, we proposed a novel approach to rank alternative complex networks
based on their performances. We consider this as a ranking problem in decision
analysis based on occurring positive/negative frequent events as criteria, and
using the TOPSIS method to rank alternatives. In order to assign a score to the
networks for each criterion, a statistical method that estimates the expected
value of positive/negative frequent events on a random node is presented. The
proposed technique is efficient in terms of algorithm complexity and is capable
of discriminating events occurring between important nodes over those between
less significant nodes. The experiments, conducted on several synthetic
networks, demonstrate the feasibility and applicability of the ranking
methodology.

Social and Information Networks;Physics and Society