  Humans communicate using systems of interconnected stimuli or concepts --
from language and music to literature and science -- yet it remains unclear
how, if at all, the structure of these networks supports the communication of
information. Although information theory provides tools to quantify the
information produced by a system, traditional metrics do not account for the
inefficient ways that humans process this information. Here we develop an
analytical framework to study the information generated by a system as
perceived by a human observer. We demonstrate experimentally that this
perceived information depends critically on a system's network topology.
Applying our framework to several real networks, we find that they communicate
a large amount of information (having high entropy) and do so efficiently
(maintaining low divergence from human expectations). Moreover, we show that
such efficient communication arises in networks that are simultaneously
heterogeneous, with high-degree hubs, and clustered, with tightly-connected
modules -- the two defining features of hierarchical organization. Together,
these results suggest that many communication networks are constrained by the
pressures of information transmission, and that these pressures select for
specific structural features.
