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Nestedness Maximization in Complex Networks through the Fitness-Complexity Algorithm

1
URPP Social Networks, University of Zurich, CH-8050 Zurich, Switzerland
2
Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu 610054, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(10), 768; https://doi.org/10.3390/e20100768
Received: 31 July 2018 / Revised: 25 September 2018 / Accepted: 25 September 2018 / Published: 8 October 2018
(This article belongs to the Special Issue Economic Fitness and Complexity)
Nestedness refers to the structural property of complex networks that the neighborhood of a given node is a subset of the neighborhoods of better-connected nodes. Following the seminal work by Patterson and Atmar (1986), ecologists have been long interested in revealing the configuration of maximal nestedness of spatial and interaction matrices of ecological communities. In ecology, the BINMATNEST genetic algorithm can be considered as the state-of-the-art approach for this task. On the other hand, the fitness-complexity ranking algorithm has been recently introduced in the economic complexity literature with the original goal to rank countries and products in World Trade export networks. Here, by bringing together quantitative methods from ecology and economic complexity, we show that the fitness-complexity algorithm is highly effective in the nestedness maximization task. More specifically, it generates matrices that are more nested than the optimal ones by BINMATNEST for 61.27% of the analyzed mutualistic networks. Our findings on ecological and World Trade data suggest that beyond its applications in economic complexity, the fitness-complexity algorithm has the potential to become a standard tool in nestedness analysis. View Full-Text
Keywords: economic fitness; fitness-complexity; genetic algorithms; nestedness temperature; ecological networks economic fitness; fitness-complexity; genetic algorithms; nestedness temperature; ecological networks
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MDPI and ACS Style

Lin, J.-H.; Tessone, C.J.; Mariani, M.S. Nestedness Maximization in Complex Networks through the Fitness-Complexity Algorithm. Entropy 2018, 20, 768. https://doi.org/10.3390/e20100768

AMA Style

Lin J-H, Tessone CJ, Mariani MS. Nestedness Maximization in Complex Networks through the Fitness-Complexity Algorithm. Entropy. 2018; 20(10):768. https://doi.org/10.3390/e20100768

Chicago/Turabian Style

Lin, Jian-Hong; Tessone, Claudio J.; Mariani, Manuel S. 2018. "Nestedness Maximization in Complex Networks through the Fitness-Complexity Algorithm" Entropy 20, no. 10: 768. https://doi.org/10.3390/e20100768

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