Centrality anomalies in complex networks as a result of model over-simplification
Tremendous advances have been made in our understanding of the properties and evolution of complex networks. These advances were initially driven by information-poor empirical networks and theoretical analysis of unweighted and undirected graphs. Recently, information-rich empirical data complex net...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2020-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ab687c |
| _version_ | 1797750293135360000 |
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| author | Luiz G A Alves Alberto Aleta Francisco A Rodrigues Yamir Moreno Luís A Nunes Amaral |
| author_facet | Luiz G A Alves Alberto Aleta Francisco A Rodrigues Yamir Moreno Luís A Nunes Amaral |
| author_sort | Luiz G A Alves |
| collection | DOAJ |
| description | Tremendous advances have been made in our understanding of the properties and evolution of complex networks. These advances were initially driven by information-poor empirical networks and theoretical analysis of unweighted and undirected graphs. Recently, information-rich empirical data complex networks supported the development of more sophisticated models that include edge directionality and weight properties, and multiple layers. Many studies still focus on unweighted undirected description of networks, prompting an essential question: how to identify when a model is simpler than it must be? Here, we argue that the presence of centrality anomalies in complex networks is a result of model over-simplification. Specifically, we investigate the well-known anomaly in betweenness centrality for transportation networks, according to which highly connected nodes are not necessarily the most central. Using a broad class of network models with weights and spatial constraints and four large data sets of transportation networks, we show that the unweighted projection of the structure of these networks can exhibit a significant fraction of anomalous nodes compared to a random null model. However, the weighted projection of these networks, compared with an appropriated null model, significantly reduces the fraction of anomalies observed, suggesting that centrality anomalies are a symptom of model over-simplification. Because lack of information-rich data is a common challenge when dealing with complex networks and can cause anomalies that misestimate the role of nodes in the system, we argue that sufficiently sophisticated models be used when anomalies are detected. |
| first_indexed | 2024-03-12T16:31:35Z |
| format | Article |
| id | doaj.art-7f2e686da0d047c39e5e4cd5e180233d |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:31:35Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-7f2e686da0d047c39e5e4cd5e180233d2023-08-08T15:28:44ZengIOP PublishingNew Journal of Physics1367-26302020-01-0122101304310.1088/1367-2630/ab687cCentrality anomalies in complex networks as a result of model over-simplificationLuiz G A Alves0https://orcid.org/0000-0001-6204-5552Alberto Aleta1https://orcid.org/0000-0002-1192-8707Francisco A Rodrigues2https://orcid.org/0000-0002-0145-5571Yamir Moreno3https://orcid.org/0000-0002-0895-1893Luís A Nunes Amaral4https://orcid.org/0000-0002-3762-789XDepartment of Chemical and Biological Engineering, Northwestern University , Evanston, IL 60208, United States of America; Institute of Mathematics and Computer Science, University of São Paulo , São Carlos, SP 13566-590, BrazilDepartment of Theoretical Physics, University of Zaragoza , Zaragoza, E-50009, Spain; Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza , Zaragoza, E-50009, SpainInstitute of Mathematics and Computer Science, University of São Paulo , São Carlos, SP 13566-590, Brazil; Mathematics Institute, University of Warwick , Gibbet Hill Road, Coventry CV4 7AL, United Kingdom; Centre for Complexity Science, University of Warwick , Coventry CV4 7AL, United KingdomDepartment of Theoretical Physics, University of Zaragoza , Zaragoza, E-50009, Spain; Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza , Zaragoza, E-50009, Spain; ISI Foundation , Turin, I-10126, ItalyDepartment of Chemical and Biological Engineering, Northwestern University , Evanston, IL 60208, United States of America; Department of Physics and Astronomy, Northwestern University , Evanston, IL 60208, United States of America; Northwestern Institute on Complex Systems (NICO), Northwestern University , Evanston, IL 60208, United States of AmericaTremendous advances have been made in our understanding of the properties and evolution of complex networks. These advances were initially driven by information-poor empirical networks and theoretical analysis of unweighted and undirected graphs. Recently, information-rich empirical data complex networks supported the development of more sophisticated models that include edge directionality and weight properties, and multiple layers. Many studies still focus on unweighted undirected description of networks, prompting an essential question: how to identify when a model is simpler than it must be? Here, we argue that the presence of centrality anomalies in complex networks is a result of model over-simplification. Specifically, we investigate the well-known anomaly in betweenness centrality for transportation networks, according to which highly connected nodes are not necessarily the most central. Using a broad class of network models with weights and spatial constraints and four large data sets of transportation networks, we show that the unweighted projection of the structure of these networks can exhibit a significant fraction of anomalous nodes compared to a random null model. However, the weighted projection of these networks, compared with an appropriated null model, significantly reduces the fraction of anomalies observed, suggesting that centrality anomalies are a symptom of model over-simplification. Because lack of information-rich data is a common challenge when dealing with complex networks and can cause anomalies that misestimate the role of nodes in the system, we argue that sufficiently sophisticated models be used when anomalies are detected.https://doi.org/10.1088/1367-2630/ab687ccomplex networksnetwork structurebetweennesscomplex systemsreal data |
| spellingShingle | Luiz G A Alves Alberto Aleta Francisco A Rodrigues Yamir Moreno Luís A Nunes Amaral Centrality anomalies in complex networks as a result of model over-simplification New Journal of Physics complex networks network structure betweenness complex systems real data |
| title | Centrality anomalies in complex networks as a result of model over-simplification |
| title_full | Centrality anomalies in complex networks as a result of model over-simplification |
| title_fullStr | Centrality anomalies in complex networks as a result of model over-simplification |
| title_full_unstemmed | Centrality anomalies in complex networks as a result of model over-simplification |
| title_short | Centrality anomalies in complex networks as a result of model over-simplification |
| title_sort | centrality anomalies in complex networks as a result of model over simplification |
| topic | complex networks network structure betweenness complex systems real data |
| url | https://doi.org/10.1088/1367-2630/ab687c |
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