Lucas Böttcher, Mason A. Porter
In many scientific applications, it is common to use binary (i.e., unweighted) edges in the study of networks to examine collections of entities that are either adjacent or not adjacent. Researchers have generalized such binary networks to incorporate edge weights, which allow one to encode node–node interactions with heterogeneous intensities or frequencies (e.g., in transportation networks, supply chains, and social networks). Most such studies have considered real-valued weights, despite the fact that networks with complex weights arise in fields as diverse as quantum information, quantum chemistry, electrodynamics, rheology, and machine learning. Many of the standard approaches from network science that originated in the study of classical systems and are based on real-valued edge weights cannot be applied directly to networks with complex edge weights. In this paper, we examine how standard network-analysis methods fail to capture structural features of networks with complex weights. We then generalize several network measures to the complex domain and show that random-walk centralities provide a useful tool to examine node importances in networks with complex weights.

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Marco Pangallo, Alberto Aleta, R. Maria del Rio Chanona, Anton Pichler, David Martín-Corral, Matteo Chinazzi, François Lafond, Marco Ajelli, Esteban Moro, Yamir Moreno, Alessandro Vespignani, J. Doyne Farmer
The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic. Epidemic-economic models designed to address this issue are either too aggregate to consider heterogeneous outcomes across socio-economic groups, or, when sufficiently fine-grained, not well grounded by empirical data. To fill this gap, we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations, and income levels with geographic realism. The key mechanism coupling the epidemic and economic modules is the reduction in consumption demand due to fear of infection. We calibrate the model to the first wave of COVID-19 in the New York metropolitan area, showing that it reproduces key epidemic and economic statistics, and then examine counterfactual scenarios. We find that: (a) both high fear of infection and strict restrictions similarly harm the economy but reduce infections; (b) low-income workers bear the brunt of both the economic and epidemic harm; (c) closing non-customer-facing industries such as manufacturing and construction only marginally reduces the death toll while considerably increasing unemployment; and (d) delaying the start of protective measures does little to help the economy and worsens epidemic outcomes in all scenarios. We anticipate that our model will help designing effective and equitable non-pharmaceutical interventions that minimize disruptions in the face of a novel pandemic.

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 Alvaro Diaz-Ruelas

Chaos 32, 123136 (2022)

The incorporation of stochastic ingredients in models describing phenomena in all disciplines is now a standard in scientific practice. White noise is one of the most important of such stochastic ingredients. Although tools for identifying white and other types of noise exist,1,2 there is a permanent demand for reliable and robust statistical methods for analyzing data in order to distinguish noise and filter it from signals in experiments. Or in hypothesis tests, for assessing the plausibility of the outcome of an experiment being the result of randomness and not a significant, controllable effect. Due to its ubiquity in experiments and its mathematical simplicity, white noise is very often the most convenient stochastic component that adds realism to a dynamic model, commonly regarded as the noise polluting observations. It can be continuous or discrete both in time and in distribution, so it can be applied to many scenarios. It is a stationary and independent and identically distributed process, all relatively simple properties for a stochastic process. Here, we present a combinatorial perspective to study white noise inspired in the concept of ordinal patterns.

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Yang Tian, Zeren Tan, Hedong Hou, Guoqi Li, Aohua Cheng, Yike Qiu, Kangyu Weng, Chun Chen, Pei Sun

Network Neuroscience (2022) 6 (4): 1148–1185.

The brain criticality hypothesis is one of the most focused and controversial topics in neuroscience and biophysics. This research develops a unified framework to reformulate the physics theories of four basic types of brain criticality, ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), into more accessible and neuroscience-related forms. For the statistic techniques used to validate the brain criticality hypothesis, we also present comprehensive explanations of them, summarize their error-prone details, and suggest possible solutions. This framework may help resolve potential controversies in studying the brain criticality hypothesis, especially those arising from the misconceptions about the theoretical foundations of brain criticality.

Read the full article at: direct.mit.edu

Ivan Shpurov and Tom Froese

Entropy 2022, 24(12), 1840

Social insects such as honey bees exhibit complex behavioral patterns, and their distributed behavioral coordination enables decision-making at the colony level. It has, therefore, been proposed that a high-level description of their collective behavior might share commonalities with the dynamics of neural processes in brains. Here, we investigated this proposal by focusing on the possibility that brains are poised at the edge of a critical phase transition and that such a state is enabling increased computational power and adaptability. We applied mathematical tools developed in computational neuroscience to a dataset of bee movement trajectories that were recorded within the hive during the course of many days. We found that certain characteristics of the activity of the bee hive system are consistent with the Ising model when it operates at a critical temperature, and that the system’s behavioral dynamics share features with the human brain in the resting state.

Read the full article at: www.mdpi.com