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Node and Edge Removal on Complex Networks in Labor Market Research and their Influence on Centrality Measures

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dc.contributor.authorMangroliya, Meetkumar Pravinbhai
dc.contributor.authorDörpinghaus, Jens
dc.contributor.authorRockenfeller, Robert
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:18Z
dc.date.available2024-10-21T18:24:18Z
dc.date.issued2024
dc.description.abstractThis research examines the impact of node and edge removal strategies on centrality measures within complex networks. Investigating random, scale-free, and small-world networks, various removal approaches, including targeted and random removal, are evaluated. The study assesses their influence on centrality metrics such as degree, betweenness, closeness, and eigenvector centrality on random networks and networks from educational research describing longitudinal data in labor market-related topics in social networks. The findings contribute insights applicable across domains. In social network analysis, an understanding of key actors is beneficial for the development of targeted interventions or marketing strategies. Historical network analyses benefit from the discernment of pivotal nodes or connections, which elucidate information flow or influential figures across different periods. Such applications underscore the significance of the research in optimizing network performance in diverse contexts.en
dc.identifier.doi10.18420/inf2024_177
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45155
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectCentrality Measures
dc.subjectComplex networks
dc.subjectLabor Market Networks
dc.subjectSocial Network Analysis
dc.titleNode and Edge Removal on Complex Networks in Labor Market Research and their Influence on Centrality Measuresen
dc.typeText/Conference Paper
gi.citation.endPage2046
gi.citation.publisherPlaceBonn
gi.citation.startPage2035
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleDigitalization and AI for and in Education and Educational Research (DAI-EaR'24)

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