L
Laura Hernández
Researcher at Cergy-Pontoise University
Publications - 44
Citations - 371
Laura Hernández is an academic researcher from Cergy-Pontoise University. The author has contributed to research in topics: Nestedness & Monte Carlo method. The author has an hindex of 11, co-authored 44 publications receiving 292 citations. Previous affiliations of Laura Hernández include University of Paris & Institut national des sciences appliquées.
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Breaking the Spell of Nestedness: The Entropic Origin of Nestedness in Mutualistic Systems
TL;DR: The nested configuration of mutually beneficial interactions among species in real ecosystems arises from the number of interactions of each species, a potentially useful insight for understanding the scale at which natural selection operates in those systems.
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The joint influence of competition and mutualism on the biodiversity of mutualistic ecosystems
TL;DR: In this article, a multilayer network that naturally accounts for mutualism and competition is built up, and a dynamical population model and numerical simulations are used to show that there is an intricate relation between competition and mutualism.
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Coevolutionary network approach to cultural dynamics controlled by intolerance
TL;DR: A rewiring probability is introduced, enabling agents to cut the links with their unfriendly neighbors if their cultural similarity is below a tolerance parameter, resulting in a multicultural society even for values of initial cultural diversity in which the original Axelrod model reaches globalization.
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Two classes of bipartite networks: nested biological and social systems.
TL;DR: A generalization of a previous model, that evolves the topology of the graph in order to optimally account for a given contact preference rule between the two guilds of the network, provides a very good fitting of all the main statistical features.
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Breaking the spell of nestedness
TL;DR: It is shown that nestedness is not an irreducible feature, but a consequence of the degree sequences of both guilds of the mutualistic network, and that an outstanding majority of the analyzed networks does not show statistical significant nestedness.