Consensus ranking for multi-objective interventions in multiplex networks
High-centrality nodes have disproportionate influence on the behavior of a network; therefore controlling such nodes can efficiently steer the system to a desired state. Existing multiplex centrality measures typically rank nodes assuming the layers are qualitatively similar. Many real systems, howe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2019-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ab14b3 |
| _version_ | 1827872784656629760 |
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| author | Márton Pósfai Niklas Braun Brianne A Beisner Brenda McCowan Raissa M D’Souza |
| author_facet | Márton Pósfai Niklas Braun Brianne A Beisner Brenda McCowan Raissa M D’Souza |
| author_sort | Márton Pósfai |
| collection | DOAJ |
| description | High-centrality nodes have disproportionate influence on the behavior of a network; therefore controlling such nodes can efficiently steer the system to a desired state. Existing multiplex centrality measures typically rank nodes assuming the layers are qualitatively similar. Many real systems, however, are comprised of networks heterogeneous in nature, for example, social networks may have both agnostic and affiliative layers. Here, we use rank aggregation methods to identify intervention targets in multiplex networks when the structure, the dynamics, and our intervention goals are qualitatively different for each layer. Our approach is to rank the nodes separately in each layer considering their different function and desired outcome, and then we use Borda count or Kemeny aggregation to identify a consensus ranking—top nodes in the consensus ranking are expected to effectively balance the competing goals simultaneously among all layers. To demonstrate the effectiveness of consensus ranking, we apply our method to a degree-based node removal procedure such that we aim to destroy the largest component in some layers, while maintaining large-scale connectivity in others. For any multi-objective intervention, optimal targets only exist in the Pareto-sense; we, therefore, use a weighted generalization of consensus ranking to investigate the trade-off between the competing objectives. We use a collection of model and real networks to systematically investigate how this trade-off is affected by multiplex network structure. We use the copula representation of the multiplex centrality distributions to generate model multiplex networks with given rank correlations. This allows us to separately manipulate the marginal centrality distribution of each layer and the interdependence between the layers, and to investigate the role of the two using both analytical and numerical methods. |
| first_indexed | 2024-03-12T16:28:48Z |
| format | Article |
| id | doaj.art-c21474bbb07a424fbba87a49800d0752 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:28:48Z |
| publishDate | 2019-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-c21474bbb07a424fbba87a49800d07522023-08-08T15:36:26ZengIOP PublishingNew Journal of Physics1367-26302019-01-0121505500110.1088/1367-2630/ab14b3Consensus ranking for multi-objective interventions in multiplex networksMárton Pósfai0Niklas Braun1Brianne A Beisner2Brenda McCowan3Raissa M D’Souza4Complexity Sciences Center and Department of Computer Science, University of California , Davis, CA 95616, United States of AmericaDepartment of Mechanical and Aerospace Engineering, University of California , Davis, CA 95616, United States of AmericaDepartment of Population Health and Reproduction, University of California , Davis, CA 95616, United States of America; Neuroscience and Behavior Unit, California National Primate Research Center, University of California , Davis, CA 95616, United States of AmericaDepartment of Population Health and Reproduction, University of California , Davis, CA 95616, United States of America; Neuroscience and Behavior Unit, California National Primate Research Center, University of California , Davis, CA 95616, United States of AmericaComplexity Sciences Center and Department of Computer Science, University of California , Davis, CA 95616, United States of America; Department of Mechanical and Aerospace Engineering, University of California , Davis, CA 95616, United States of America; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, United States of AmericaHigh-centrality nodes have disproportionate influence on the behavior of a network; therefore controlling such nodes can efficiently steer the system to a desired state. Existing multiplex centrality measures typically rank nodes assuming the layers are qualitatively similar. Many real systems, however, are comprised of networks heterogeneous in nature, for example, social networks may have both agnostic and affiliative layers. Here, we use rank aggregation methods to identify intervention targets in multiplex networks when the structure, the dynamics, and our intervention goals are qualitatively different for each layer. Our approach is to rank the nodes separately in each layer considering their different function and desired outcome, and then we use Borda count or Kemeny aggregation to identify a consensus ranking—top nodes in the consensus ranking are expected to effectively balance the competing goals simultaneously among all layers. To demonstrate the effectiveness of consensus ranking, we apply our method to a degree-based node removal procedure such that we aim to destroy the largest component in some layers, while maintaining large-scale connectivity in others. For any multi-objective intervention, optimal targets only exist in the Pareto-sense; we, therefore, use a weighted generalization of consensus ranking to investigate the trade-off between the competing objectives. We use a collection of model and real networks to systematically investigate how this trade-off is affected by multiplex network structure. We use the copula representation of the multiplex centrality distributions to generate model multiplex networks with given rank correlations. This allows us to separately manipulate the marginal centrality distribution of each layer and the interdependence between the layers, and to investigate the role of the two using both analytical and numerical methods.https://doi.org/10.1088/1367-2630/ab14b3multiplex networksnode rankingcentrality measures |
| spellingShingle | Márton Pósfai Niklas Braun Brianne A Beisner Brenda McCowan Raissa M D’Souza Consensus ranking for multi-objective interventions in multiplex networks New Journal of Physics multiplex networks node ranking centrality measures |
| title | Consensus ranking for multi-objective interventions in multiplex networks |
| title_full | Consensus ranking for multi-objective interventions in multiplex networks |
| title_fullStr | Consensus ranking for multi-objective interventions in multiplex networks |
| title_full_unstemmed | Consensus ranking for multi-objective interventions in multiplex networks |
| title_short | Consensus ranking for multi-objective interventions in multiplex networks |
| title_sort | consensus ranking for multi objective interventions in multiplex networks |
| topic | multiplex networks node ranking centrality measures |
| url | https://doi.org/10.1088/1367-2630/ab14b3 |
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