{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:35:12Z","timestamp":1760240112265,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T00:00:00Z","timestamp":1552435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2014even via intermediaries\u2014is then calculated applying the principle of maximum entropy. The usefulness of this new approach is demonstrated by the analysis of an affiliation network called \u201ccorporate directors\u201d.<\/jats:p>","DOI":"10.3390\/e21030277","type":"journal-article","created":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T04:15:29Z","timestamp":1552536929000},"page":"277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Bipartite Structures in Social Networks: Traditional versus Entropy-Driven Analyses"],"prefix":"10.3390","volume":"21","author":[{"given":"Wilhelm","family":"R\u00f6dder","sequence":"first","affiliation":[{"name":"Department of Operations Research, FernUniversit\u00e4t in Hagen, 58097 Hagen, Germany"}]},{"given":"Andreas","family":"Dellnitz","sequence":"additional","affiliation":[{"name":"Department of Quantitative Methods, FernUniversit\u00e4t in Hagen, 58097 Hagen, Germany"}]},{"given":"Friedhelm","family":"Kulmann","sequence":"additional","affiliation":[{"name":"Department of Quantitative Methods, FernUniversit\u00e4t in Hagen, 58097 Hagen, Germany"}]},{"given":"Sebastian","family":"Litzinger","sequence":"additional","affiliation":[{"name":"Department of Quantitative Methods, FernUniversit\u00e4t in Hagen, 58097 Hagen, Germany"}]},{"given":"Elmar","family":"Reucher","sequence":"additional","affiliation":[{"name":"Department of Business Administration, Private Hochschule f\u00fcr Wirtschaft und Technik, 49377 Vechta, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Moreno, J. 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