Chao-Ran Cai, Yuan-Yuan Nie, Petter Holme

Analytical studies of network epidemiology almost exclusively focus on the extreme situations where the time scales of network dynamics are well separated (longer or shorter) from that of epidemic propagation. In realistic scenarios, however, these time scales could be similar, which has profound implications for epidemic modeling (e.g., one can no longer reduce the dimensionality of epidemic models). We build a theory for the critical behavior of susceptible-infected-susceptible (SIS) epidemics in the vicinity of the critical threshold on the activity-driven model of temporal networks. We find that the persistence of links in the network leads to increasing recovery rates reducing the threshold. Dynamic correlations (coming from being close to infected nodes increases the likelihood of infection) drive the threshold in the opposite direction. These two counteracting effects make epidemic criticality in temporal networks a remarkably complex phenomenon.

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Modern scientists aren’t content with predicting how life evolves. They want to shape it.

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Haily Merritt, Gabriel J. Severino, Eduardo J. Izquierdo

Artificial Life

We offer three advances to the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition. First, we evolve and systematically test agents in rigorous conditions, identifying a set of 26 “robust circuits” with consistently high and generalizing performance. Next, we transform the sensor from discrete to continuous, facilitating a bifurcation analysis of the dynamics that shows that nonequilibrium dynamics are key to the mutual maintenance of interaction. Finally, we examine agents’ performance with partners whose neural controllers are different from their own and with decoy objects of fixed frequency and amplitude. Nonclonal performance varies and is not predicted by genotypic distance. Frequency-amplitude values that fool the focal agent do not include the agent’s own values. Altogether, our findings accentuate the importance of dynamical and nonclonal analyses for simulated sociality, emphasize the role of dialogue between artificial and human studies, and highlight the contributions of simulation studies to understanding social interactions.

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Clàudia Payrató-Borràs, Carlos Gracia-Lázaro, Laura Hernández, Yamir Moreno

Mutualistic interactions, where species interact to obtain mutual benefits, constitute an essential component of natural ecosystems. The use of ecological networks to represent the species and their ecological interactions allows the study of structural and dynamic patterns common to different ecosystems. However, by neglecting the temporal dimension of mutualistic communities, relevant insights into the organization and functioning of natural ecosystems can be lost. Therefore, it is crucial to incorporate empirical phenology — the cycles of species’ activity within a season — to fully understand the effects of temporal variability on network architecture. In this paper, by using two empirical datasets together with a set of synthetic models, we propose a framework to characterize phenology on ecological networks and assess the effect of temporal variability. Analyses reveal that non-trivial information is missed when portraying the network of interactions as static, which leads to overestimating the value of fundamental structural features. We discuss the implications of our findings for mutualistic relationships and intra-guild competition for common resources. We show that recorded interactions and species’ activity duration are pivotal factors in accurately replicating observed patterns within mutualistic communities. Furthermore, our exploration of synthetic models underscores the system-specific character of the mechanisms driving phenology, increasing our understanding of the complexities of natural ecosystems.

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Manuel Miranda, Gissell Estrada-Rodriguez, and Ernesto Estrada

Entropy 2023, 25(12), 1599

Geometric realization of simplicial complexes makes them a unique representation of complex systems. The existence of local continuous spaces at the simplices level with global discrete connectivity between simplices makes the analysis of dynamical systems on simplicial complexes a challenging problem. In this work, we provide some examples of complex systems in which this representation would be a more appropriate model of real-world phenomena. Here, we generalize the concept of metaplexes to embrace that of geometric simplicial complexes, which also includes the definition of dynamical systems on them. A metaplex is formed by regions of a continuous space of any dimension interconnected by sinks and sources that works controlled by discrete (graph) operators. The definition of simplicial metaplexes given here allows the description of the diffusion dynamics of this system in a way that solves the existing problems with previous models. We make a detailed analysis of the generalities and possible extensions of this model beyond simplicial complexes, e.g., from polytopal and cell complexes to manifold complexes, and apply it to a real-world simplicial complex representing the visual cortex of a macaque.

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