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Entropy 2019, 21(3), 277;

Bipartite Structures in Social Networks: Traditional versus Entropy-Driven Analyses

Department of Operations Research, FernUniversität in Hagen, 58097 Hagen, Germany
Department of Quantitative Methods, FernUniversität in Hagen, 58097 Hagen, Germany
Department of Business Administration, Private Hochschule für Wirtschaft und Technik, 49377 Vechta, Germany
Author to whom correspondence should be addressed.
Received: 15 February 2019 / Revised: 6 March 2019 / Accepted: 7 March 2019 / Published: 13 March 2019
(This article belongs to the Special Issue Entropy and Information in Networks, from Societies to Cities)
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A special type of social networks is the so-called affiliation network, consisting of two modes of vertices: actors and events. Up to now, in the undirected case, the closeness of actors in such networks has been measured by their jointly-attended events. Indirect contacts and attenuated and directed links are of minor interest in affiliation networks. These flaws make a veritable estimation of, e.g., possible message transfers amongst actors questionable. In this contribution, first, we discuss these matters from a graph-theoretical point of view. Second, so as to avoid the identified weaknesses, we propose an up-and-coming entropy-based approach for modeling such networks in their generic structure, replacing directed (attenuated) links by conditionals: if-then. In this framework, the contribution of actors and events to a reliable message transfer from one actor to another—even via intermediaries—is then calculated applying the principle of maximum entropy. The usefulness of this new approach is demonstrated by the analysis of an affiliation network called “corporate directors”. View Full-Text
Keywords: social network analysis; bipartite structures; directed graphs; attenuated links; entropy social network analysis; bipartite structures; directed graphs; attenuated links; entropy

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Rödder, W.; Dellnitz, A.; Kulmann, F.; Litzinger, S.; Reucher, E. Bipartite Structures in Social Networks: Traditional versus Entropy-Driven Analyses. Entropy 2019, 21, 277.

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