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26 April 2024 |
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Anomaly detection in complex networks as a diagnosis of model over-simplification | | Luiz G. A. Alves
; Alberto Aleta
; Francisco A. Rodrigues
; Yamir Moreno
; Luis A. Nunes Amaral
; | | Date: |
2 Feb 2019 | | 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 provides a
diagnosis 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 four large datasets, we show that the unweighted projection of the
structure of the inter-city bus transportation network and the worldwide air
transportation network 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, no longer show these
anomalies, 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. | | Source: | arXiv, 1902.0716 | | Services: | Forum | Review | PDF | Favorites | |
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