The impact of partially missing communities on the reliability of centrality measures

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Authors

Network data is usually not error-free, and the absence of some nodes is a very common type of measurement error. Studies have shown that the reliability of centrality measures is severely affected by missing nodes. This paper investigates the reliability of centrality measures when missing nodes are likely to belong to the same community. We study the behavior of five commonly used centrality measures in uniform and scale-free networks in various error scenarios. We find that centrality measures are generally more reliable when missing nodes are likely to belong to the same community than in cases in which nodes are missing uniformly at random. In scale-free networks, the betweenness centrality becomes, however, less reliable when missing nodes are more likely to belong to the same community. Moreover, centrality measures in scale-free networks are more reliable in networks with stronger community structure. In contrast, we do not observe this effect for uniform networks. Our observations suggest that the impact of missing nodes on the reliability of centrality measures might not be as severe as the literature suggests.

Original languageEnglish
Title of host publicationComplex Networks & Their Applications VI : Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications)
Number of pages12
Volume1
PublisherSpringer Verlag
Publication date2018
Pages41-52
ISBN (print)978-3-319-72149-1
ISBN (electronic)978-3-319-72150-7
DOIs
Publication statusPublished - 2018
Event6th International Conference on Complex Networks and Their Applications - Complex Networks 2017 - Lyon, France
Duration: 29.11.201701.12.2017
Conference number: 6
http://past.complexnetworks.org/index2017.html

Bibliographical note

Fachgebiete: "Network Science", "Information Systems"

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