Network link prediction by global silencing of indirect correlations

Predicting physical and functional links between cellular components is a fundamental challenge of biology and network science. Yet, correlations, a ubiquitous input for biological link prediction, are affected by both direct and indirect effects, confounding our ability to identify true pairwise interactions. Here we exploit the fundamental properties of dynamical correlations in networks to develop a method to silence indirect effects. The method receives as input the observed correlations between node pairs and uses a matrix transformation to turn the correlation matrix into a highly discriminative silenced matrix, which enhances only the terms associated with direct causal links. Achieving perfect accuracy in model systems, we test the method against empirical data collected for the Escherichia coli regulatory interaction network, showing that it improves on the best preforming link prediction methods. Overall the silencing methodology helps translate the abundant correlation data into valuable local information, with applications ranging from link prediction to inferring the dynamical mechanisms governing biological networks

Universality in network dynamics

Despite significant advances in characterizing the structural properties of complex networks, a mathematical framework that uncovers the universal properties of the interplay between the topology and the dynamics of complex systems continues to elude us. Here we develop a self-consistent theory of dynamical perturbations in complex systems, allowing us to systematically separate the contribution of the network topology and dynamics. The formalism covers a broad range of steady-state dynamical processes and offers testable predictions regarding the system's response to perturbations and the development of correlations. It predicts several distinct universality classes whose characteristics can be derived directly from the continuum equation governing the system's dynamics and which are validated on several canonical network-based dynamical systems, from biochemical dynamics to epidemic spreading. Finally, we collect experimental data pertaining to social and biological systems, demonstrating that we can accurately uncover their universality class even in the absence of an appropriate continuum theory that governs the system's dynamics

Constructing cost-effective infrastructure networks

The need for reliable and low-cost infrastructure is crucial in today's world. However, achieving both at the same time is often challenging. Traditionally, infrastructure networks are designed with a radial topology lacking redundancy, which makes them vulnerable to disruptions. As a result, network topologies have evolved towards a ring topology with only one redundant edge and, from there, to more complex mesh networks. However, we prove that large rings are unreliable. Our research shows that a sparse mesh network with a small number of redundant edges that follow some design rules can significantly improve reliability while remaining cost-effective. Moreover, we have identified key areas where adding redundant edges can impact network reliability the most by using the SAIDI index, which measures the expected number of consumers disconnected from the source node. These findings offer network planners a valuable tool for quickly identifying and addressing reliability issues without the need for complex simulations. Properly planned sparse mesh networks can thus provide a reliable and a cost-effective solution to modern infrastructure challenges

Perfect synchronization in networks of phase-frustrated oscillators

Synchronizing phase frustrated Kuramoto oscillators, a challenge that has found applications from neuronal networks to the power grid, is an eluding problem, as even small phase-lags cause the oscillators to avoid synchronization. Here we show, constructively, how to strategically select the optimal frequency set, capturing the natural frequencies of all oscillators, for a given network and phase-lags, that will ensure perfect synchronization. We find that high levels of synchronization are sustained in the vicinity of the optimal set, allowing for some level of deviation in the frequencies without significant degradation of synchronization. Demonstrating our results on first and second order phase-frustrated Kuramoto dynamics, we implement them on both model and real power grid networks, showing how to achieve synchronization in a phase frustrated environment.Comment: To appear in Europhysics Letters, 7 pages, supplementary informatio