Each complex network (or class of networks) presents specific topological features which
characterize its connectivity and highly influence the dynamics of processes executed on the …

The success of new scientific areas can be assessed by their potential in contributing to new
theoretical approaches and in applications to real-world problems. Complex networks have …

This research draws on longitudinal network data from an online community to examine
patterns of users' behavior and social interaction, and infer the processes underpinning …

With the aim to measure and monitor systemic risk, we present some topological metrics for
the interbank exposures and the payments system networks. The evolution of such networks …

Graph theoretical analysis has played a key role in characterizing global features of the
topology of complex networks, describing diverse systems such as protein interactions, food …

Transmission of weak signals in neural networks is crucial for understanding the
functionality of brain. In this work, stochastic resonance (SR) in the three neuron FitzHugh …

Approximating ideal program outputs is a common technique for solving computationally
difficult problems, for adhering to processing or timing constraints, and for performance …

Research on the robustness of networks, and in particular the Internet, has gained critical
importance in recent decades because more and more individuals, societies and firms rely …

This paper proposes a resilience-constrained unit commitment (RCUC) framework in which
the system operation constraints, heterogeneity of power flow distribution and lines forced …

We analyze the time evolution of systemic risk in Europe by using different entropy
measures and construct a new early warning indicator for banking crises. The analysis is …