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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/63784


    Title: On Team Formation with Expertise Query in Collaborative Social Networks
    Authors: 劉惠美
    Li, Cheng-Te;Shan, Man-Kwan;Lin, Shou-De
    Contributors: 統計系
    Keywords: Team formation;Social network;Expertise query;Collaborative networks
    Date: 2013.08
    Issue Date: 2014-02-11 14:02:07 (UTC+8)
    Abstract: Given a collaborative social network and a task consisting of a set of required skills, the team formation problem aims at finding a team of experts who not only satisfies the requirements of the given task but also is able to communicate with one another in an effective manner. This paper extends the original team formation problem to a generalized version, in which the number of experts selected for each required skill is also specified. The constructed teams need to contain adequate number of experts for each required skill. We develop two approaches to compose teams for the proposed generalized team formation tasks. First, we consider the specific number of experts to devise the generalized Enhanced-Steiner algorithm. Second, we present a grouping-based method condensing the expertise information to a compact representation, group graph, based on the required skills. Group graph can not only reduce the search space but also eliminate redundant communication cost and filter out irrelevant individuals when compiling team members. To further improve the effectiveness of the composed teams, we propose a density-based measure and embed it into the developed methods. Experimental results on the DBLP network show that the teams composed by the proposed methods have better performance in both effectiveness and efficiency.
    Relation: Knowledge and Information Systems, February 2015, Volume 42, Issue 2, pp 441-463
    10.1007/s10115-013-0695-x
    Data Type: article
    DOI link: http://dx.doi.org/10.1007/s10115-013-0695-x
    DOI: 10.1007/s10115-013-0695-x
    Appears in Collections:[Department of Computer Science ] Periodical Articles

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