Bio

Hana Khamfroush is a postdoctoral scholar in the Electrical Engineering and Computer Science department of Penn State University, working with Prof. Thomas La Porta. She received her PhD with highest distinction from the University of Porto in Portugal and in Collaboration with Aalborg University of Denmark in Nov. 2014. Her PhD research focused on network coding for cooperation in dynamic wireless networks. Currently at PSU, she is working on interdependent networks, network recovery and network tomography. Her research interests include complex networks, computer networks, wireless communications, and mathematical models. She received a four-year scholarship from the ministry of science of Portugal for her PhD, and was awarded many grants and fellowships from the European Union. Recently, she received the best poster award for her recent work in the basic research technical review meeting of DTRA.

On Propagation of Phenomena in Interdependent Networks

On Propagation of Phenomena in Interdependent Networks

Operational networks of different types are often interdependent and interconnected. Many of today’s infrastructures are organized in the form of interdependent networks. For example, the smart grid is controlled via the Internet, and the Internet is powered by the smart grid. A failure in one may lead to service degradation and possibly failure in the other. This failure procedure can cascade multiple times between the two interdependent networks and therefore, results in catastrophic widespread failures. Previous works that are modeling the interdependency between two networks are generally based on strong assumptions and specific applications, thus fail to capture important aspects of real networks. Furthermore, most of the previous works only address the asymptotic behavior of the networks. To fill this gap, we focused on the temporal evolution of the phenomena propagation in interdependent networks. The goal is to identify the importance of the nodes in terms of their influence on the propagation phenomenon, and to design more efficient interdependent networks.

We proposed a general theoretical model for such a propagation, which captures several possible models of interaction among affected nodes. Our model is general in the sense that there is no assumption on the network topology, propagation model, or the capability of the network nodes (heterogeneity of the networks). The theoretical model allows us to evaluate small-scale networks. On the other hand, we implemented a simulator, which allows for the evaluation of larger scale networks for different types of random graphs, different models of coupling between networks, and different initial spreaders. Based on our analysis, we propose a new centrality metric designed for the interdependent networks that is shown to be more precise in identifying the importance of the nodes compared to the traditional centrality metrics. Our next step would be analyzing the phenomena propagation in time-varying interdependent networks.