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On network analysis using non-additive integrals: extending the game-theoretic network centrality
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Hamilton Institute, Maynooth University, Ireland. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-0368-8037
Department of Management Science, Tamagawa University, Japan.
2019 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 23, no 7, p. 2321-2329Article in journal (Refereed) Published
Abstract [en]

There are large amounts of information that can be represented in terms of graphs. This includes social networks and internet. We can represent people and their interactions by means of graphs. Similarly, we can represent web pages (and sites) as well as links between pages by means of graphs. In order to study the properties of graphs, several indices have been defined. They include degree centrality, betweenness, and closeness. In this paper, we propose the use of Choquet and Sugeno integrals with respect to non-additive measures for network analysis. This is a natural extension of the use of game theory for network analysis. Recall that monotonic games in game theory are non-additive measures. We discuss the expected force, a centrality measure, in the light of non-additive integral network analysis. We also show that some results by Godo et al. can be used to compute network indices when the information associated with a graph is qualitative.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 23, no 7, p. 2321-2329
Keywords [en]
Non-additive measures and integrals, Graphs, Aggregation, Network analysis
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-16741DOI: 10.1007/s00500-018-03710-9ISI: 000461580400016Scopus ID: 2-s2.0-85059054946OAI: oai:DiVA.org:his-16741DiVA, id: diva2:1302282
Part of project
Disclosure risk and transparency in big data privacy, Swedish Research Council
Funder
Swedish Research Council, 2016-03346
Note

CC BY 4.0

Correspondence to Vicenç Torra.

Partial support by the Swedish Research Council (Vetenskapsrådet) project DRIAT (VR 2016-03346)

Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2021-08-18Bibliographically approved

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Torra, Vicenç

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