Bin Zhou, Petter Holme, Zaiwu Gong, Choujun Zhan, Yao Huang, Xin Lu & Xiangyi Meng 

Nature Communications volume 14, Article number: 7031 (2023)

Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node’s weight, reflecting its innate attractiveness (nature), and the node’s degree, reflecting the external influences (nurture). The proposed model provides a good fit for degree distributions and degree ratio distributions of numerous real-world networks and reproduces their evolution processes. Our results indicate that the nurture factor plays a dominant role in the evolution of social networks. In contrast, the nature factor plays a dominant role in the evolution of non-social networks, suggesting that whether nodes are people determines the dominant factor influencing the evolution of real-world networks.

Read the full article at: www.nature.com

Ricard Solé

Preserving and restoring biodiversity is becoming a great challenge as we face a world where planetary boundaries will likely be crossed over the following decades. Such challenge needs to consider multiple scales of complexity, both in space and time. A common thread in most cases is the presence of nonlinear phenomena generating shifts among alternative states. These breaking points imply a new perception of risk and different management strategies. A broad range of phenomena affect the preservation of healthy communities and constrain the ways to deal with conservation, from local features associated with habitat loss or facilitation to mesoscale or global network-level ecological complexity and the role played by extreme events. How are these scales connected? How can the emergent properties associated with ecosystem dynamics be exploited? Here a synthesis of ideas is presented, with a complex systems view of the different scales involved, the emergent phenomena separating them, and the universal properties that allow defining simple models on each scale.

Read the full article at: www.preprints.org

Georg Börner, Hauke Haehne, Jose Casadiego, Marc Timme

Chaos 33, 073136 (2023)

The dynamics of a complex system is fundamentally governed by the number of its active dynamical variables, the system’s state space dimension. However, identifying state space dimension constitutes a difficult task, in particular if the dimension is much larger than the number of variables observed. Here, we show that it is mathematically possible in principle to infer the dimension of the state space using time series observations of just one variable, for arbitrarily high state space dimensions. We discuss how in practice the success of this inference depends on numerical constraints of data evaluation and experimental choices, such as the sampling intervals and total duration of observations. We illustrate how the approach may be applied to high-dimensional systems, e.g., with 100 variables, and provide general rules of thumb for performing and evaluating measurements of a given system. Our results provide a novel approach for inferring the dimension of complex and networked dynamical systems from scalar time series data and may help to develop alternative methods, e.g., for the reconstruction of the dimensions of system attractors.

Read the full article at: pubs.aip.org

Didier Le Bail, Mathieu Génois, Alain Barrat

Although many tools have been developed and employed to characterize temporal networks, the issue of how to compare them remains largely open. It depends indeed on what features are considered as relevant, and on the way the differences in these features are quantified. In this paper, we propose to characterize temporal networks through their behaviour under general transformations that are local in time: (i) a local time shuffling, which destroys correlations at time scales smaller than a given scale b, while preserving large time scales, and (ii) a local temporal aggregation on time windows of length n. By varying b and n, we obtain a flow of temporal networks, and flows of observable values, which encode the phenomenology of the temporal network on multiple time scales. We use a symbolic approach to summarize these flows into labels (strings of characters) describing their trends. These labels can then be used to compare temporal networks, validate models, or identify groups of networks with similar labels. Our procedure can be applied to any temporal network and with an arbitrary set of observables, and we illustrate it on an ensemble of data sets describing face-to-face interactions in various contexts, including both empirical and synthetic data.

Read the full article at: arxiv.org

Luís F. Seoane and Ricard Solé

Phys. Rev. E 108, 044407

Why are living systems complex? Why does the biosphere contain living beings with complexity features beyond those of the simplest replicators? What kind of evolutionary pressures result in more complex life forms? These are key questions that pervade the problem of how complexity arises in evolution. One particular way of tackling this is grounded in an algorithmic description of life: living organisms can be seen as systems that extract and process information from their surroundings to reduce uncertainty. Here we take this computational approach using a simple bit string model of coevolving agents and their parasites. While agents try to predict their worlds, parasites do the same with their hosts. The result of this process is that, to escape their parasites, the host agents expand their computational complexity despite the cost of maintaining it. This, in turn, is followed by increasingly complex parasitic counterparts. Such arms races display several qualitative phases, from monotonous to punctuated evolution or even ecological collapse. Our minimal model illustrates the relevance of parasites in providing an active mechanism for expanding living complexity beyond simple replicators, suggesting that parasitic agents are likely to be a major evolutionary driver for biological complexity.

Read the full article at: link.aps.org