學術產出-Periodical Articles

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

題名 On Team Formation with Expertise Query in Collaborative Social Networks
作者 劉惠美
Li, Cheng-Te ; Shan, Man-Kwan ; Lin, Shou-De
貢獻者 統計系
關鍵詞 Team formation; Social network; Expertise query; Collaborative networks
日期 2013.08
上傳時間 11-Feb-2014 14:02:07 (UTC+8)
摘要 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.
關聯 Knowledge and Information Systems, February 2015, Volume 42, Issue 2, pp 441-463
10.1007/s10115-013-0695-x
資料類型 article
DOI http://dx.doi.org/10.1007/s10115-013-0695-x
dc.contributor 統計系en_US
dc.creator (作者) 劉惠美zh_TW
dc.creator (作者) Li, Cheng-Te ; Shan, Man-Kwan ; Lin, Shou-Deen_US
dc.date (日期) 2013.08en_US
dc.date.accessioned 11-Feb-2014 14:02:07 (UTC+8)-
dc.date.available 11-Feb-2014 14:02:07 (UTC+8)-
dc.date.issued (上傳時間) 11-Feb-2014 14:02:07 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/63784-
dc.description.abstract (摘要) 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.en_US
dc.format.extent 1636244 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Knowledge and Information Systems, February 2015, Volume 42, Issue 2, pp 441-463en_US
dc.relation (關聯) 10.1007/s10115-013-0695-x-
dc.subject (關鍵詞) Team formation; Social network; Expertise query; Collaborative networksen_US
dc.title (題名) On Team Formation with Expertise Query in Collaborative Social Networksen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1007/s10115-013-0695-xen_US
dc.doi.uri (DOI) http://dx.doi.org/10.1007/s10115-013-0695-xen_US