An analytic strategy for data processing of multimode networks

Vincenzo Giuseppe Genova (), Giuseppe Giordano (), Giancarlo Ragozini () and Maria Prosperina Vitale ()
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Vincenzo Giuseppe Genova: University of Palermo
Giuseppe Giordano: University of Salerno
Giancarlo Ragozini: University of Naples Federico II
Maria Prosperina Vitale: University of Salerno

Advances in Data Analysis and Classification, 2024, vol. 18, issue 3, No 10, 745-767

Abstract: Abstract Complex network data structures are considered to capture the richness of social phenomena and real-life data settings. Multipartite networks are an example in which various scenarios are represented by different types of relations, actors, or modes. Within this context, the present contribution aims at discussing an analytic strategy for simplifying multipartite networks in which different sets of nodes are linked. By considering the connection of multimode networks and hypergraphs as theoretical concepts, a three-step procedure is introduced to simplify, normalize, and filter network data structures. Thus, a model-based approach is introduced for derived bipartite weighted networks in order to extract statistically significant links. The usefulness of the strategy is demonstrated in handling two application fields, that is, intranational student mobility in higher education and research collaboration in European framework programs. Finally, both examples are explored using community detection algorithms to determine the presence of groups by mixing up different modes.

Keywords: Multipartite networks; Data simplification; Data filtering; Normalization; Real-data examples (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11634-023-00556-4

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