We study the evolutionary Prisoner's Dilemma on two social networks obtained
from actual relational data. We find very different cooperation levels on each
of them that can not be easily understood in terms of global statistical
properties of both networks. We claim that the result can be understood at the
mesoscopic scale, by studying the community structure of the networks. We
explain the dependence of the cooperation level on the temptation parameter in
terms of the internal structure of the communities and their interconnections.
We then test our results on community-structured, specifically designed
artificial networks, finding perfect agreement with the observations in the
real networks. Our results support the conclusion that studies of evolutionary
games on model networks and their interpretation in terms of global properties
may not be sufficient to study specific, real social systems. In addition, the
community perspective may be helpful to interpret the origin and behavior of
existing networks as well as to design structures that show resilient
cooperative behavior.Comment: Largely improved version, includes an artificial network model that
fully confirms the explanation of the results in terms of inter- and
intra-community structur