  Empirical studies of graphs have contributed enormously to our understanding
of complex systems. Known today as network science, what was originally a
theoretical study of graphs has grown into a more scientific exploration of
communities spanning the physical, biological, and social. However, as the
quantity and types of networks have grown so has their heterogeneity in quality
and specificity. This has hampered efforts to develop general network theory
capable of inferring functioning and predicting dynamics across study systems.
We have successfully approached this challenge by aligning networks to each
other rather than comparing parameter estimates from individually fitted models
or properties of edge topologies. By comparing the predictability of
statistical dynamics originating from each network's constituent nodes we were
able to build a functional classifier that distinguished underlying processes
in both synthetic and real-world network data spanning the entire biological
scale from cellular machinery to ecosystems.
