Maximizing information exchange between complex networks

摘要: Abstract Science is not merely the smooth progressive interaction of hypothesis, experiment and theory, although it sometimes has that form. More realistically scientific study any given complex phenomenon generates a number explanations, from variety perspectives, eventually requires synthesis to achieve deep level insight understanding. One such created field out-of-equilibrium statistical physics as applied understanding dynamic networks. Over past forty years concept complexity undergone metamorphosis. Complexity was originally seen consequence memory in individual particle trajectories, full agreement with Hamiltonian picture microscopic dynamics and, principle, macroscopic could be derived picture. The main difficulty deriving need take into account actions very large components. existence events abrupt jumps, considered by conventional continuous time random walk approach describing never perceived conflicting view. Herein we review many reasons why this traditional view unsatisfactory. We show result technological advances, which make observation single elementary possible, definition shifted towards action non-Poisson renewal events. crucial processes, intermittent fluorescence blinking quantum dots well brain’s response music, monitored set electrodes attached scalp, forced investigators go beyond establish closer contact nascent Complex networks form one most challenging areas modern research overarching all disciplines. transportation planes, highways railroads; economic global finance stock markets; social terrorism, governments, businesses churches; physical telephones, Internet, earthquakes warming biological gene regulation, human body, clusters neurons food webs, share apparently universal properties become increasingly complex. Ubiquitous aspects are appearance non-stationary non-ergodic processes inverse power-law distributions. dynamical phase–space methods for modeling their increases focus on limitations these procedures explaining Of course will able entire network science, so limit ourselves how certain barriers have been surmounted using newly theoretical concepts aging, renewal, statistics fractional calculus. emphasis information transport between networks, fundamental change perception express transition familiar stochastic resonance new matching.