Social Network Formation and Strategic Interaction in Large Networks

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dc.contributor.authorShin, Euncheolko
dc.date.accessioned2021-06-12T08:10:22Z-
dc.date.available2021-06-12T08:10:22Z-
dc.date.created2021-03-24-
dc.date.created2021-03-24-
dc.date.issued2021-05-
dc.identifier.citationMATHEMATICAL SOCIAL SCIENCES, v.111, pp.34 - 54-
dc.identifier.issn0165-4896-
dc.identifier.urihttp://hdl.handle.net/10203/285803-
dc.description.abstractThis paper presents a bilateral network formation model that explains why some empirical degree distributions exhibit the increasing hazard rate property (IHRP). In my model, a sequentially arriving node forms a link with an existing node through a bilateral agreement. This newborn node prefers a highly linked node; however, the more links an existing node have, the more the marginal return from an additional link diminishes. I prove that the IHRP emerges if and only if the latter effect prevails over the former. I provide two implications of the IHRP. First, I show that the IHRP is related to the existence of equilibria in network games with strategic complementarities. Second, I fit the model to empirical degree distributions and demonstrate that the IHRP is more likely to be observed in social and economic networks. For those networks, my model also exhibits a higher data-fitting performance than other distributions.-
dc.languageEnglish-
dc.publisherELSEVIER-
dc.titleSocial Network Formation and Strategic Interaction in Large Networks-
dc.typeArticle-
dc.identifier.wosid000649635100005-
dc.identifier.scopusid2-s2.0-85104296593-
dc.type.rimsART-
dc.citation.volume111-
dc.citation.beginningpage34-
dc.citation.endingpage54-
dc.citation.publicationnameMATHEMATICAL SOCIAL SCIENCES-
dc.identifier.doi10.1016/j.mathsocsci.2021.01.006-
dc.contributor.localauthorShin, Euncheol-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorDegree distribution-
dc.subject.keywordAuthorIncreasing hazard rate property-
dc.subject.keywordAuthorNetwork game-
dc.subject.keywordAuthorRandom network-
dc.subject.keywordPlusMODEL-
dc.subject.keywordPlusDISTRIBUTIONS-
dc.subject.keywordPlusGRAPHS-
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