Amir Bashan

TL;DR: In this paper, the authors develop a framework to identify and quantify dynamical networks of diverse systems with different types of interactions, and find that each physiological state is characterized by a specific network structure, demonstrating a robust interplay between network topology and function.

TL;DR: These investigations are initial steps in building a first atlas of dynamic interactions among organ systems, which demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems.

TL;DR: Analysis of real-world interdependent networks shows that randomly positioned networks, where nodes are positioned according to geographical constraints, might not be so resilient.

TL;DR: A detailed comparison between the regular DFA and two recently suggested methods: the Centered Moving Average (CMA) Method and a Modified Detrended Fluctuation Analysis (MDFA) is presented, finding that CMA performs the same as DFA in long data with weak trends and is slightly superior to D FA in short data with strong trends.

TL;DR: The study of cascading failures in a system composed of two interdependent square lattice networks A and B suggests that interdependent infrastructures embedded in Euclidean space become most vulnerable when the distance between interdependent nodes is in the intermediate range, which is much smaller than the size of the system.