Frontiers | Fractional Dynamics of Individuals in Complex Networks | Physics
Award Abstract Collaborative research: Complex dynamics, criticality and cascading events in power system blackouts and communication networks. These blackouts severely impact the public, commerce, and government and are a vulnerability in the nation's infrastructure. Rather than focusing on the detailed causes of individual blackouts, this project addresses the global, complex system dynamics of a long series of blackouts. New models, simulation and analysis methods will be developed to capture the essentials of criticality, self-organization and cascading failure.
Particular attention will be given to the power tails of probability distributions of blackout sizes that occur in North American blackout data and generally in systems at criticality. These power tails imply that large, catastrophic blackouts are much more likely than predicted by conventional risk analyses. The project will develop new risk analysis methods and explore operating techniques to mitigate the risks of major blackouts.
Communication networks such as the Internet also develop major cascading disruptions and work will also be pursued in this application to maintain some focus on universal features of disruptions in large, engineered networks of societal importance. The expected outcomes are the ability to understand, statistically analyze and to some extent mitigate major cascading disruptions in power transmission and communication networks.
The scientific outcomes are an improved understanding and new application of complex system dynamics to the engineering and analysis of stressed networks. Here we use a dynamic metric to predict the actual spatio-temporal propagation patterns. Spatio-temporal propagation of signals in complex networks. Spectrum of controlling and observing complex networks. Dynamic patterns of information flow in complex networks. Constructing minimal models for complex system dynamics.
Universal resilience patterns in complex networks. Universality in network dynamics. Dynamic patterns of information flow in complex networks , Nature Communications 8 , 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.
Complex Dynamics in Communication Networks / Edition 1
For instance how the blind interactions between pairs of magnetic spins lead to the seemingly cooperative phenomena of magnetism. 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.
Dynamic information routing in complex networks
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.
Our work was covered by Makor Rishon. Makor Rishon Dyokan. Channel 2 News. The Silencing Method. News NEU.
Universality in Network Dynamics. Spatiotemporal propagation of signals in complex networks. Universal Resilience Patterns in Complex Networks.
Joint network topology and dynamics recovery from perturbed stationary points. The metastability of the double-tripod gait in locust locomotion. Physical Review E 98 , Perfect synchronization in networks of phase-frustrated oscillators. Europhysics Letters , , Patterns of information flow in complex networks.
- Complex Dynamics in Communication Networks?
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Uzi Harush and Baruch Barzel. Nature Communications 8 , Nature , Nature Communications 6 , Nature Physics 11 , Network link prediction by global silencing of indirect correlations. When analyzing such scenarios, static community discovery algorithms fail to identify partitions semantically consistent with the temporal information expressed by the data. Our algorithm, differently from the ones proposed so far for which we provide a classification and review in  , does not discretize the dynamic problem into an ordered sequence of static ones, conversely TILES is designed to work on atomic local perturbations of the dynamic network i.
Such design choice solves two major issues introduced by previous approaches: i it avoids the need to identify meaningful temporal annotated network snapshots which is a context dependent problem , and ii it allows to track community dynamics across time without adopting matching strategies.
We showed that our approach is able to outperform state of art competitors in providing interpretable dynamics of overlapping communities on real world case studies built upon communication networks having different semantics call graphs, Facebook comments, Sina Weibo direct messages. So far one of the most adopted strategy to evaluate community discovery algorithms has involved the comparison of the topologies they are able to identify with ground truth ones.
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