Exact Rank Reduction of Network Models

Valdano, Eugenio; Arenas, Àlex

doi:10.1103/physrevx.9.031050

Cited by 2 publications

(2 citation statements)

References 54 publications

(73 reference statements)

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“…3a. Such no-correlation scenario leads the resulting matrix to be in the universality class of so-called rank-1 networks 40 , and can also be understood under the paradigm of fitness-mediated good-get-richer networks 41 or a particular class of hidden variable network 42 .…”

Section: Phytocentric Embedding: Function-function Network and Multif...mentioning

confidence: 99%

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The architecture of multifunctional ecological networks

Hervías-Parejo,

Cuevas-Blanco,

Lacasa

et al. 2023

Preprint

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Understanding how biotic interactions affect ecosystem functioning has been a research priority in natural sciences due to their critical role in bolstering ecological resilience. Yet, traditional assessment of ecological complexity typically focus on species-species effective interactions that mediate a particular function (e.g. pollination or seed dispersa), overlooking the synergistic effect of multiple functions that further underpin species-function and function-function interactions in multifunctional ecosystems. At the same time, while ecological network theory holds a potential to quantify the relationship between biodiversity and ecosystem multifunctionality, its connection has been done mainly conceptually, due to challenges measuring different interactions and establishing their relevance across multiple niche dimensions. Such lack of quantitative studies therefore limits our ability to determine which species and interactions are important to maintain the multiple functions of ecosystems. Here we develop a framework -derived from a resource-consumer-function tensor analysis- that bridges these gaps by framing biodiversity-ecosystem multifunctionality in terms of multilayer ecological network theory. Its application to recently collected ecological data -reporting weighted interactions between plants, animals and fungi across multiple function types- allows to (i) unveil and quantify the existence of both (multi-functional) keystone species and a dual function keystoneness pattern, and (ii) project plants and functions into a similarity space where clear clusters emerge and the importance of weak links is manifested. This dual insight from species and functional perspectives will better guide conservation efforts to reduce biodiversity loss.

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Section: Phytocentric Embedding: Function-function Network and Multif...mentioning

confidence: 99%

“…The finding that woody shrubs are those more strongly involved in different functions might be c The conditioned plant-plant networks are again in the universality class of rank-1 networks 40 and can be interpreted as fitness-based or hidden variable networks 41,42 .…”

Section: Phytocentric Embedding: Function-function Network and Multif...mentioning

confidence: 99%

The architecture of multifunctional ecological networks

Hervías-Parejo,

Cuevas-Blanco,

Lacasa

et al. 2023

Preprint

View full text Add to dashboard Cite

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Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices

Di Domenico,

Valdano,

Colizza

2024

Phys. Rev. Research

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Traditional disease transmission models assume that the infectious period is exponentially distributed with a recovery rate fixed in time and across individuals. This assumption provides analytical and computational advantages, however, it is often unrealistic when compared to empirical data. Current efforts in modeling nonexponentially distributed infectious periods are either limited to special cases or lead to unsolvable models. Also, the link between empirical data (the infectious period distribution) and the modeling needs (the definition of the corresponding recovery rates) lacks a clear understanding. Here we introduce a mapping of an arbitrary distribution of infectious periods into a distribution of recovery rates. Under the Markovian assumption to ensure analytical tractability, we show that the same infectious period distribution at the population level can be reproduced by two modeling schemes that we call and , depending on the individual response to the infection, and aggregated empirical data cannot easily discriminate the correct scheme. Besides being conceptually different, the two schemes also lead to different epidemic trajectories. Although sharing the same behavior close to the disease-free equilibrium, the scheme deviates from the expected epidemic when reaching the endemic equilibrium of a susceptible-infectious-susceptible transmission model, while the scheme turns out to be equivalent to assuming a homogeneous recovery rate. We show this through analytical computations and stochastic epidemic simulations on a contact network, using both generative network models and empirical contact data. It is therefore possible to reproduce heterogeneous infectious periods in network-based transmission models, however, the resulting prevalence is sensitive to the modeling choice for the interpretation of the empirically collected data on the length of the infectious period. In the absence of higher resolution data, studies should acknowledge such deviations in the epidemic predictions. Published by the American Physical Society 2024

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Exact Rank Reduction of Network Models

Cited by 2 publications

References 54 publications

The architecture of multifunctional ecological networks

The architecture of multifunctional ecological networks

Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices

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