Bringing networks to life.
From sub-cellular biology to the Internet, networks capture the architecture behind complex behavior. They map the pathways that channel genetic information between cellular components, spread viruses among linked individuals and help neuronal signals propagate between brain regions. But the network is just the static architecture underlying these rich dynamics. How does this structure translate into dynamic behavior?
At the Complex Network Dynamics lab we translate structure into function. Predicting how signals spread along network pathways, uncovering the network components that contribute to the system’s stability and resilience and detecting the nodes and links that enable information to flow throughout the system. Our ultimate goal is to systematically use complex network data to understand, predict and control its observed behavior.
So, what can your network do?
Evolving patterns of epidemic flow
While topology is static - the patterns of flow can change with time. Watch as the flow shifts with time from the hubs to the peripheral nodes. Read more
Signal propagation in complex networks
How does a signal travel in a network environment? Unpredictably. Here we use a dynamic metric to predict the actual spatio-temporal propagation patterns. Read more
Dynamic patterns of information flow in complex networks, Nature Communications 8, 2181 (2017)
Dynamics of Complex Networks
Statistical physics is the theory of interacting particles, gases and liquids. Its way of thought, however, goes beyond the domain of material science. In a broader perspective it provides us with a bridge between the microscopic description of a system and its observed macroscopic behavior. With it we can track the way in which system-level phenomena emerge from the mechanistic description of the system’s interacting components. For instance how the blind interactions between pairs of magnetic spins lead to the seemingly cooperative phenomena of magnetism.
At CND we develop the statistical physics of complex systems: our interacting particles are not atoms or spins, but rather genes, proteins, animal species or humans. We track the way in which individual human interactions lead to the spread of ideas, perceptions and also diseases, or how biochemical reactions between proteins transfer information between cellular components. These systems defy many of the classic principles that are central to the way physics is traditionally done. The particles are self-driven and non-Newtonian, the interactions are nonlinear and the underlying geometry in random, highly irregular and non-localized. In two words – complex systems.
With these challenges at hand, we find that the dynamic behavior of these complex systems – social, biological or technological – can be predicted, analyzed and understood using the tools and way of thought of statistical physics.
To get a popular taste of our scientific research please visit the links below
From Neurons to Facebook - Presentation at Nitzozot
Universal Resilience Patterns in Complex Networks video
An interview with American Friends of Bar-Ilan University
Editing by Lonnie Ostrow