  In both natural and engineered systems, communication often occurs
dynamically over networks ranging from highly structured grids to largely
disordered graphs. To use, or comprehend the use of, networks as efficient
communication media requires understanding of how they propagate and transform
information in the face of noise. Here, we develop a framework that enables us
to examine how network structure, noise, and interference between consecutive
packets jointly determine transmission performance in networks with linear
dynamics at single nodes and arbitrary topologies. Mathematically normal
networks, which can be decomposed into separate low-dimensional information
channels, suffer greatly from readout and interference noise. Interestingly,
most details of their wiring have no impact on transmission quality. Non-normal
networks, however, can largely cancel the effect of noise by transiently
amplifying select input dimensions while ignoring others, resulting in higher
net information throughput. Our theory could inform the design of new
communication networks, as well as the optimal use of existing ones.
