Our ability to understand, predict and influence network dynamics is hindered by a crucial lacuna: the absence of validated microscopic models to describe the inner mechanisms of many complex systems. In simple words, we do not know network’s internal equation of motion. Our approach is to identify macroscopic Observables that can be extracted from data, then directly linked to the network’s hidden mechanisms. These Observables become a measurable fingerprint by which to expose the system’s dynamics directly from data.
Real networks constantly change over time, as links switch between active and inactive states. In social systems these temporal patterns emerge from individual human behavior. In turn, they also impact the patterns of information propagation, social influence and viral spread. What are the rules that govern temporal networks, how are they linked to human behavior and mobility, and what is their returning impact on our behavior? Updates will be added as the data releases its hidden secrets.
Universal network characteristics, such as the scale-free degree distribution and the small world phenomena, are the bread and butter of network science. But how do we translate such topological findings into an understanding of the system's dynamic behavior: for instance, how does the small world structure impact the patterns of flow in the system? Or how does the presence of hubs affect the distribution of influence?
In essence, whether its communicable diseases, genetic regulation or the spread of failures in an infrastructure network, these questions touch upon 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 topology. We show that we can tame this zoo, by exposing a systematic translation of topological elements into their dynamic outcome – uncovering the deep universality behind the seemingly diverse dynamics.
Influencing the behaviour of a complex network represents the ultimate challenge of our research on network dynamics. We seek to develop interventions to drive a network system in a desired direction.
Can we restore a failed eco-system by controlling just a few nodes?
Can we mitigate a cascading power failure in real time by strategically shutting down selected components?
How should we disseminate drugs when a global pandemic spreads through air-
travel? This turns into a competition between drugs and pathogens - both racing to
reach the majority of the population first. Our Digitizable therapeutics project aims to bypass this competition by disseminating DNA-based therapeutics via email. The drugs spread as data, then printed locally on location.