nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

Robustness of Centrality Measures Under Incomplete Data

verfasst von : Natalia Meshcheryakova, Sergey Shvydun

Erschienen in: Complex Networks & Their Applications XII

Verlag: Springer Nature Switzerland

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Understanding of real systems relies on the identification of its central elements. Over the years, a large number of centrality measures have been proposed to assess the importance of nodes in complex networks. However, most real networks are incomplete and contain incorrect data, resulting in a high sensitivity of centrality indices. In this paper, we examine the robustness of centrality to the presence of errors in the network structure. Our experiments are performed on weighted and unweighted real-world networks ranging from the criminal network to the trade food network. As a result, we discuss a sensitivity of centrality measures to different data imputation techniques.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel IS-PEW: Identifying Influential Spreaders Using Potential Edge Weight in Complex Networks

Nächstes Kapitel ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives

In the case of node addition, we assume that the added node was initially isolated.

The dominating set of centrality \(x\) includes a list of centrality measures, which are more sensitive than \(x\) to the graph modification based on the pairwise comparison. Similarly, the dominated set of \(x\) contains centralities that are less sensitive than \(x\).

We have also performed the analysis for \(k=10\%\) and the overall results are highly agreed with \(k=5\%\), even though the centrality measures are less stable.

Centiserver: The most comprehensive centrality resource and web application for centrality measures calculation (2023). https://www.centiserver.org/centrality/list/. Accessed 1 Jul 2023

Newman, M.E.J.: Networks: An Introduction. Oxford University Press, Oxford (2010). https://doi.org/10.1093/acprof:oso/9780199206650.001.0001CrossRef

Myerson, R.B.: Graphs and cooperation games. Math. Oper. Res. 2, 225–229 (1977). https://doi.org/10.1287/moor.2.3.225MathSciNetCrossRef

Kang, C., Molinaro, C., Kraus, S., Shavitt, Y., Subrahmanian, V.S.: Diffusion centrality in social networks. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 558–664, Istanbul (2012). https://doi.org/10.1109/ASONAM.2012.95

Aleskerov, F., Shvydun, S., Meshcheryakova, N.: New Centrality Measures in Networks: How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes (1st ed.). Chapman and Hall/CRC (2021). https://doi.org/10.1201/9781003203421

Ficara, A., et al.: Criminal networks analysis in missing data scenarios through graph distances. PLoS ONE 16(8), e0255067 (2021). https://doi.org/10.1371/journal.pone.0255067CrossRef

Aleskerov, F., Andrievskaya, I., Nikitina, A., Shvydun, S.: Key Borrowers Detected by the Intensities of Their Interactions. Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (In 4 Volumes), 355–389 World Scientific: Singapore Volume 1, Chapter 9 (2020). https://doi.org/10.1142/9789811202391_0009

Meshcheryakova, N.: Network analysis of bilateral trade data under asymmetry. In: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), The Hague, Netherlands, pp. 379–383 (2020). https://doi.org/10.1109/ASONAM49781.2020.9381408

Borgatti, S.P., Carley, K.M., Krackhardt, D.: On the robustness of centrality measures under conditions of imperfect data. Soc. Networks 28(2), 124–136 (2006). https://doi.org/10.1016/j.socnet.2005.05.001CrossRef

10.

Frantz, T.L., Cataldo, M., Carley, K.M.: Robustness of centrality measures under uncertainty: Examining the role of network topology. Comput. Math. Organ. Theory 15(4), 303–328 (2009). https://doi.org/10.1007/s10588-009-9063-5

11.

Segarra, S., Ribeiro, A.: Stability and continuity of centrality measures in weighted graphs. IEEE Trans. Signal Process. 64(3), 543–555 (2016). https://doi.org/10.1109/ICASSP.2015.7178599MathSciNetCrossRef

12.

Martin, C., Niemeyer, P.: Influence of measurement errors on networks: estimating the robustness of centrality measures. Network Sci. 7(2), 180–195 (2019). https://doi.org/10.1017/nws.2019.12CrossRef

13.

Murai, S., Yoshida, Y.: Sensitivity analysis of centralities on unweighted networks. In: The World Wide Web Conference on - WWW 2019, pp. 1332–1342 (2019). https://doi.org/10.1145/3308558.3313422

14.

Meshcheryakova, N., Shvydun S.: Perturbation analysis of centrality measures. In: 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE (2023). https://doi.org/10.1145/3625007.3627590

15.

Bolland, J.M.: Sorting out centrality: an analysis of the performance of four centrality models in real and simulated networks. Soc. Networks 10(3), 233–253 (1988). https://doi.org/10.1016/0378-8733(88)90014-7MathSciNetCrossRef

16.

Herland, M., Pastran, P., Zhu, X.: An empirical study of robustness of network centrality scores in various networks and conditions. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, pp. 221–228 (2013). https://doi.org/10.1109/ICTAI.2013.42

17.

Niu, Q., Zeng, A., Fan, Y., Di, Z.: Robustness of centrality measures against network manipulation. Physica A 438, 124–131 (2015). https://doi.org/10.1016/j.physa.2015.06.031CrossRef

18.

Krause, R.W., Huisman, M., Steglich, C., Snijders, T.A.B.: Missing network data a comparison of different imputation methods. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, pp. 159–163 (2018). https://doi.org/10.1109/ASONAM.2018.8508716

19.

Kossinets, G.: Effects of missing data in social networks. Soc. Networks 28(3), 247–268 (2006). https://doi.org/10.1016/j.socnet.2005.07.002CrossRef

20.

Saari, D.G., Merlin, V.R.: The Copeland method: I.: relationships and the dictionary. Econ. Theory 8(1), 51–76 (1996)MathSciNet

21.

Shvydun, S.: Normative properties of multi-criteria choice procedures and their superpositions: I. Working paper WP7/2015/07 (Part 1). Moscow: HSE Publishing House (2015). https://doi.org/10.48550/arXiv.1611.00524

22.

Ficara, A., et al.: Social network analysis of Sicilian mafia interconnections. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds.) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol. 882. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36683-4_36

23.

Cavallaro, L., Ficara, A., De Meo, P., Fiumara, G., Catanese, S., et al.: Disrupting resilient criminal networks through data analysis: the case of Sicilian Mafia. PLoS ONE 15(8), e0236476 (2020). https://doi.org/10.1371/journal.pone.0236476CrossRef

24.

The World Integrated Trade Solution (2023). https://wits.worldbank.org/. Accessed 1 Sept 2023

Titel: Robustness of Centrality Measures Under Incomplete Data
verfasst von: Natalia Meshcheryakova
Sergey Shvydun
Verlag: Springer Nature Switzerland
Buch: Complex Networks & Their Applications XII
Print ISBN: 978-3-031-53471-3

Electronic ISBN: 978-3-031-53472-0

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-53472-0_27

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner