Centrality anomalies in complex networks as a result of model over-simplification

Author(s)

Alves, Luiz G. A. ^{[1, 2]} ; Aleta, Alberto ^{[3, 4]} ; Rodrigues, Francisco A. ^{[1, 5, 6]} ; Moreno, Yamir ^{[3, 4, 7]} ; Amaral, Luis A. Nunes ^{[8, 2, 9]}

Affiliation

^[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil

^[2] Northwestern Univ, Dept Chem & Biol Engn, Evanston, IL 60208 - USA

^[3] Univ Zaragoza, Inst Biocomputat & Phys Complex Syst BIFI, E-50009 Zaragoza - Spain

^[4] Univ Zaragoza, Dept Theoret Phys, E-50009 Zaragoza - Spain

^[5] Univ Warwick, Ctr Complex Sci, Coventry CV4 7AL, W Midlands - England

^[6] Univ Warwick, Math Inst, Gibbet Hill Rd, Coventry CV4 7AL, W Midlands - England

^[7] ISI Fdn, I-10126 Turin - Italy

^[8] Northwestern Univ, Dept Phys & Astron, Evanston, IL 60208 - USA

^[9] Northwestern Univ, Northwestern Inst Complex Syst NICO, Evanston, IL 60208 - USA

• Document type: Journal article
• Source: NEW JOURNAL OF PHYSICS; v. 22, n. 1 JAN 2020.
• Web of Science Citations: 0

Abstract

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. (AU)

• FAPESP's process: 16/25682-5 - Information spreading in complex networks
• Grantee: Francisco Aparecido Rodrigues
• Support Opportunities: Regular Research Grants
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC