Session J7: The Dynamics of Co-Evolving and Interdependent Networks

Robustness of Interdependent Networks

In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of many interdependent networks. We will present a framework for understanding the robustness of interacting networks subject to such cascading failures and provide a basic analytic approach that may be useful in future studies. We present exact analytical solutions for the critical fraction of nodes that upon removal will lead to a failure cascade and to a complete fragmentation of two interdependent networks in a first order transition [1]. Surprisingly, analyzing complex systems as a set of interdependent networks may alter a basic assumption that network theory has relied on: while for a single network a broader degree distribution of the network nodes results in the network being more robust to random failures, for interdependent networks, the broader the distribution is, the more vulnerable the networks become to random failure. We also show [2] that reducing the coupling between the networks leads to a change from a first order percolation phase transition to a second order percolation transition at a critical point. These findings pose a significant challenge to the future design of robust networks that need to consider the unique properties of interdependent networks. \\[4pt] [1] S. Buldyrev, R. Parshani, G. Paul, H.E. Stanley, S. Havlin, Nature, 465, 0893 (2010)\\[0pt] [2] R. Parshani, S. Buldyrev, S. Havlin, PRL, 105, 048701 (2010)   

Interconnected Financial Networks

It is known that different dynamics on a network are associated to different resilient structures. In particular in the case of financial networks the fragility is associated to the kind of relation between the vertices (financial agents). Since the same agents can be connected on different networks for different financial relations, a study on the global resilience can only be obtained by considering the whole structure. Here we present our activity on this topic.   

Emergent phenomena in interacting networks

This abstract not available.   

Dynamical network approach for social interactions

This abstract not available.   

The Structure and Dynamics of Economic Complexity

Can network science help us understand the structure and evolution of the global economy? In this talk I summarize recent research that uses networks and complexity science to describe and explain the evolution of the mix of products that countries, and cities, produce and export. First, I show how to use information on the network connecting industries to locations to measure the complexity of an economy. Using these measures I demonstrate that countries tend to approach a level of income that is dictated by the complexity of their economies. Next, I study the evolution of economic complexity by showing that it is constrained by a coordination problem that countries, and cities, deal with using three different channels: First, they move to products that are close by, in the Product Space, to the products that they already do. Second, they are more likely to develop a product if a geographical neighbor has already developed it. And third, they follow the nestedness of the network connecting industries to locations. Finally, I introduce a simple model to account for the stylized facts uncovered in the previous sections.