Filippo Radicchi, Dmitri Krioukov, Harrison Hartle and Ginestra Bianconi

Journal of Physics: Complexity, Volume 1, Number 2

 

Existing information-theoretic frameworks based on maximum entropy network ensembles are not able to explain the emergence of heterogeneity in complex networks. Here, we fill this gap of knowledge by developing a classical framework for networks based on finding an optimal trade-off between the information content of a compressed representation of the ensemble and the information content of the actual network ensemble. We introduce a novel classical network ensemble satisfying a set of soft constraints and we find the optimal distribution of the constraints for this ensemble. We show that for the classical network ensemble in which the only constraints are the expected degrees a power-law degree distribution is optimal. Also, we study spatially embedded networks finding that the interactions between nodes naturally lead to non-uniform spread of nodes in the embedding space, leading in some cases to a fractal distribution of nodes. This result is consistent with the so called `blessing of non-uniformity’ of data, i.e. the fact that real world data typically do not obey uniform distributions. The pertinent features of real-world air transportation networks are well described by the proposed framework.

 

Source: iopscience.iop.org

Joseph E. Stiglitz

 

• Gross domestic product (GDP) is almost universally used to gauge how well a society is doing. In fact, it is a measure of market activity—no more.
• The Great Recession of 2008–2009 highlighted the need for better ways to measure the well-being of an economy and society, as well as its sustainability—whether or not good times can last.
• Over the past decade leading scholars have devised a broad set of measures to help steer societies toward the futures their citizens desire. Several countries are embedding these “dashboard” indicators into their decision-making processes.

Source: www.scientificamerican.com

His incompleteness theorems destroyed the search for a mathematical theory of everything. Nearly a century later, we’re still coming to grips with the consequences.

Source: www.quantamagazine.org

Zachary G. Nicolaou, Michael Sebek, István Z. Kiss, and Adilson E. Motter

Phys. Rev. Lett.

 

Synchronization is a widespread phenomenon observed in physical, biological, and social networks, which persists even under the influence of strong noise. Previous research on oscillators subject to common noise has shown that noise can actually facilitate synchronization, as correlations in the dynamics can be inherited from the noise itself. However, in many spatially distributed networks, such as the mammalian circadian system, the noise that different oscillators experience can be effectively uncorrelated. Here, we show that uncorrelated noise can in fact enhance synchronization when the oscillators are coupled together. Strikingly, our analysis also shows that uncorrelated noise can be more effective than common noise in enhancing synchronization. We first establish these results theoretically for phase and phase-amplitude oscillators subject to either or both additive and multiplicative noise. We then confirm the predictions through experiments on coupled electrochemical oscillators. Our findings suggest that uncorrelated noise can promote rather than inhibit coherence in natural systems and that the same effect can be harnessed in engineered systems.

Source: journals.aps.org

Valeria d’Andrea and Manlio De Domenico

Complexity

Volume 2020 Article ID 8650742

 

Equations governing the nonlinear dynamics of complex systems are usually unknown, and indirect methods are used to reconstruct their manifolds. In turn, they depend on embedding parameters requiring other methods and long temporal sequences to be accurate. In this paper, we show that an optimal reconstruction can be achieved by lossless compression of system’s time course, providing a self-consistent analysis of its dynamics and a measure of its complexity, even for short sequences. Our measure of complexity detects system’s state changes such as weak synchronization phenomena, characterizing many systems, in one step, integrating results from Lyapunov and fractal analysis.

Source: www.hindawi.com