A new centrality index designed for multilayer networks

Author:

Lotfi Nastaran1ORCID,Requejo Henrique S.2,Rodrigues Francisco A.1,Mello Marco A. R.2ORCID

Affiliation:

1. Departamento de Matemática Aplicada e Estatística Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo—Campus de São Carlos São Carlos SP Brazil

2. Departamento de Ecologia Instituto de Biociências, Universidade de São Paulo São Paulo SP Brazil

Abstract

Abstract Since its inception, the keystone species concept has become a central theoretical framework in ecology. Among many approaches, keystones have been operationalized in natural and human environments using centrality metrics applied to monolayer networks. Despite the great success of this approach, as species make several types of interactions, recent studies on keystones moved from monolayer to multilayer networks. To help fulfil the need for a centrality metric designed for multilayer networks, here we introduce Gnorm. We tested the performance of our new metric using in silico data in addition to an empirical data set of frugivory and nectarivory interactions between bats and plants in the Neotropics. A comparison between the results obtained with different random and scale‐free networks demonstrates the performance of our new metric. First, a modularity analysis based on the multilayer version of the Louvain algorithm enables the modules to be composed of nodes from different layers. Second, by setting the coupling parameter () and the resolution parameter (), module identity changes gradually, from single‐ to multiple‐node modules and from mono‐ to multilayer composition. Third, we check the number of modules from different layers to which a node belongs (G) at different levels of and . Finally, by observing how average G decreases with and , it is possible to calculate Gnorm and detect which nodes are most resistant to change in these two parameters. Those resistant nodes are identified as central in the multilayer structure. After applying this new analysis to the bat–plant network, we observed that it identified a different set of potential keystone species compared to previous analyses performed separately for each layer or the aggregated network. In conclusion, our new metric opens a new way of operationalizing the keystone species concept in multilayer networks. It may help identify keystone species involved in different interaction types.

Funder

Alexander von Humboldt-Stiftung

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

Wiley

Subject

Ecological Modeling,Ecology, Evolution, Behavior and Systematics

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3