  Precisely how humans process relational patterns of information in knowledge,
language, music, and society is not well understood. Prior work in the field of
statistical learning has demonstrated that humans process such information by
building internal models of the underlying network structure. However, these
mental maps are often inaccurate due to limitations in human information
processing. The existence of such limitations raises clear questions: Given a
target network that one wishes for a human to learn, what network should one
present to the human? Should one simply present the target network as-is, or
should one emphasize certain parts of the network to proactively mitigate
expected errors in learning? To answer these questions, we study the
optimization of network learnability. Evaluating an array of synthetic and
real-world networks, we find that learnability is enhanced by reinforcing
connections within modules or clusters. In contrast, when networks contain
significant core-periphery structure we find that learnability is best
optimized by reinforcing peripheral edges between low-degree nodes. Overall,
our findings suggest that the accuracy of human network learning can be
systematically enhanced by targeted emphasis and de-emphasis of prescribed
sectors of information.
