Dynamical robustness of collective neuronal activity upon targeted damage in interdependent networks

In the last decades, the availability of data about the structure of social, technological and biological systems has provided important insights on the mechanisms governing their correct functioning and robustness. These mechanisms are grounded on the complex backbone of interactions among the constituents of the system, which include both topological and dynamical aspects. Here, we analyze interdependent networks composed of two layers of interacting neuronal units and explore their robustness when these synthetic cultures are subjected to damage in the form of either targeted attack or failure. Our results show that the functionality of these networks does not decrease monotonically with damage but, on the contrary, they are able to increase their level of activity when the experienced damage is sufficiently strong.

Percolation on interdependent networks with different group size distributions under targeted attack

Traditional research studies on interdependent networks with groups ignore the relationship between nodes in dependency groups. In real-world networks, nodes in the same group may support each other through cooperation and tend to fail or survive together. In this paper, based on the framework of group percolation, a cascading failure model on interdependent networks with cooperative dependency groups under targeted attacks is proposed, and the effect of group size distributions on the robustness of interdependent networks is investigated. The mutually giant component and phase transition point of networks with different group size distributions are analyzed. The effectiveness of the theory is verified through simulations. Results show that the robustness of interdependent networks with cooperative dependency groups can be enhanced by increasing the heterogeneity between groups under targeted attacks. The theory can well predict the numerical simulation results. This model provides some theoretical guidance for designing robust interdependent systems in real world.

Preserving Innovation: Ensuring the Future of Today’s Scholarship

Scholars are experimenting with increasingly diverse digital technologies to express their research in new ways. Publishers, in turn, are working to support complex, dynamic, born-digital publications that can no longer be represented in print. New forms of scholarship contain enhancements such as embedded media and viewers, data visualizations, different approaches to version management, complex interdependent networks of supporting materials such as software and data, reader-contributed content (annotations, comments), interactive features, and nonlinear forms of navigation. These features can create challenges for the long-term sustainability of the publication – without planning for longevity the most innovative scholarship today may lose the characteristics that make them unique or become expensive to maintain. These challenges are magnified for preservation services that aim to ensure the publications will be available for future scholars. It is in this context that NYU Libraries initiated a project to bring together preservation services that focus on scholarly content with publishers concerned about the long-term survival of their most innovative publications. By analyzing examples of dynamic and enhanced open access monographs, the preservation services determined what could be preserved at scale using current tools. From this the team produced a set of guidelines that those involved in creating and publishing content could use to make these new forms of publications more preservable. The project was also an opportunity to start a conversation between preservation services and publishers about ways to collaborate around the shared goal of perpetuating access to unique and often costly publications.

The Robustness of Interdependent Directed Networks With Intra-layer Angular Correlations

The robustness of interdependent networks is a frontier topic in current network science. A line of studies has so far been investigated in the perspective of correlated structures on robustness, such as degree correlations and geometric correlations in interdependent networks, in-out degree correlations in interdependent directed networks, and so on. Advances in network geometry point that hyperbolic properties are also hidden in directed structures, but few studies link those features to the dynamical process in interdependent directed networks. In this paper, we discuss the impact of intra-layer angular correlations on robustness from the perspective of embedding interdependent directed networks into hyperbolic space. We find that the robustness declines as increasing intra-layer angular correlations under targeted attacks. Interdependent directed networks without intra-layer angular correlations are always robust than those with intra-layer angular correlations. Moreover, empirical networks also support our findings: the significant intra-layer angular correlations are hidden in real interdependent directed networks and contribute to the prediction of robustness. Our work sheds light that the impact of intra-layer angular correlations should be attention, although in-out degree correlations play a positive role in robustness. In particular, it provides an early warning indicator by which the system decoded the intrinsic rules for designing efficient and robust interacting directed networks.