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A new stochastic path-length tree methodology for constructing communication networks

dc.contributor.authorCho, Jaewunen_US
dc.contributor.authorDeSarbo, Wayne S.en_US
dc.date.accessioned2006-04-10T14:41:48Z
dc.date.available2006-04-10T14:41:48Z
dc.date.issued1991-06en_US
dc.identifier.citationCho, Jaewun, DeSarbo, Wayne S. (1991/06)."A new stochastic path-length tree methodology for constructing communication networks." Social Networks 13(2): 105-140. <http://hdl.handle.net/2027.42/29291>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6VD1-4697XH4-6/2/6607bdeef3fd9c1b5943f1cd908706eeen_US
dc.identifier.urihttps://hdl.handle.net/2027.42/29291
dc.description.abstractNetwork analysis has become a popular method for identifying the communication structure in a system where positional and relational aspects are important. In this paper, a maximum likelihood based methodology is presented that allows for the analysis of binary sociometric data. This methodology provides a network representation via estimated path-length or additive trees that indicate the distance between all pairs of members. The methodology is distinguished from traditional hierarchical clustering based procedures by its direct consideration of the asymmetry in a typical communication process, the simultaneous representation of structural characteristics (e.g., clique membership, clique cohesiveness), and the identification of the specialized communication roles of each member (e.g., opinion leader, liaison). A penalty function algorithm is developed and its performance is investigated via a Monte Carlo analysis with synthetic data. An application examining information flows among managers is presented. Finally, directions for future research are suggested.en_US
dc.format.extent1903700 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleA new stochastic path-length tree methodology for constructing communication networksen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelSocial Sciences (General)en_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumSchool of Business Administration, University of Michigan, Ann Arbor, MI 48109-1234, USAen_US
dc.contributor.affiliationotherCollege of Business, Arizona State University, Tempe, AZ 85287, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/29291/1/0000352.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0378-8733(91)90016-Men_US
dc.identifier.sourceSocial Networksen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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