Network GPS - Navigating network dynamics

In the past two decades we made significant advances in mapping the structure of social, biological and technological networks. The challenge that remains is to translate everything we know about network structure into its actual observed dynamics. In essence, whether it's communicable diseases, genetic regulation, or the spread of failures in an infrastructure network, these dynamics boil down to the patterns of information spread in the network. It all begins with a local perturbation, such as a sudden disease outbreak or a local power failure, which then propagates to impact all other nodes. The challenge is that the resulting spatio-temporal propagation patterns are diverse and unpredictable - indeed, a zoo of spreading patterns - that seem to be only loosely connected to the network structure. We show that we can tame this zoo by exposing a systematic translation of network structural elements into their dynamic outcome, allowing us to navigate the network, and, most importantly, to expose a deep universality behind the seemingly diverse dynamics. Along the way, we predict how long it takes for viruses to spread between countries, which network components harvest most of the system's information, and how to resuscitate a collapsed network back into functionality.