Searching for Expertise in Social Networks: A Simulation of Potential Strategies

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dc.contributor.authorZhang, Jun
dc.contributor.authorAckerman, Mark S.
dc.date.accessioned2023-06-08T11:43:57Z
dc.date.available2023-06-08T11:43:57Z
dc.date.issued2005
dc.description.abstractPeople search for people with suitable expertise all of the time in their social networks - to answer questions or provide help. Recently, efforts have been made to augment this searching. However, relatively little is known about the social characteristics of various algorithms that might be useful. In this paper, we examine three families of searching strategies that we believe may be useful in expertise location. We do so through a simulation, based on the Enron email data set. (We would be unable to suitably experiment in a real organization, thus our need for a simulation.) Our emphasis is not on graph theoretical concerns, but on the social characteristics involved. The goal is to understand the tradeoffs involved in the design of social network based searching engines.en
dc.identifier.doi10.1145/1099203.1099214
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4856
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofProceedings of the 2005 ACM International Conference on Supporting Group Work
dc.subjectCSCW
dc.subjectsocial networks
dc.subjectorganizational simulations
dc.subjectexpertise sharing
dc.subjectcomputer-supported cooperative work
dc.subjectexpertise finding
dc.subjectinformation seeking
dc.subjectexpertise location
dc.subjectsocial computing
dc.titleSearching for Expertise in Social Networks: A Simulation of Potential Strategiesen
gi.citation.publisherPlaceNew York, NY, USA
gi.citation.startPage71–80
gi.conference.locationSanibel Island, Florida, USA

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