Community Discovery for Knowledge Collaborations in Collective intelligence Systems

• Item type: JInfP(1)
• 公開日: 2014-04-15
• タイトル: Community Discovery for Knowledge Collaborations in Collective intelligence Systems
• タイトル / 言語: en
• タイトル: Community Discovery for Knowledge Collaborations in Collective intelligence Systems
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Special Issue on Multiagent-based Societal Systems] community discovery, knowledge collaborative community, multi-domain problem solving, collective intelligence
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Auckland University of Technology
• 著者所属: Auckland University of Technology
• 著者所属: Wollongong University
• 著者所属: Wollongong University
• 著者所属(英): en
• 著者所属(英): Auckland University of Technology
• 著者所属(英): en
• 著者所属(英): Auckland University of Technology
• 著者所属(英): en
• 著者所属(英): Wollongong University
• 著者所属(英): en
• 著者所属(英): Wollongong University
• 著者名: Jing, Jiang; Quan, Bai; Minjie, Zhang; Shaojie, Yuan
• 著者名(英): Jing, Jiang; Quan, Bai; Minjie, Zhang; Shaojie, Yuan
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Knowledge collaborative communities play an important role in collective intelligence systems. To discover a knowledge collaborative community, we need to consider not only the structure of a network but also the performance of knowledge collaboration among members within the community. Traditional community discovery approaches are not suitable to discover knowledge collaborative communities since most of them focus too much on the network topologies, and ignore some other important factors. In this paper, we propose two community discovery approaches, which can be used in different sizes of networks, and take more knowledge collaboration factors into account. Compared with some other existing approaches, the proposed approach can perform better in forming knowledge collaborative communities for multi-domain problem solving.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Knowledge collaborative communities play an important role in collective intelligence systems. To discover a knowledge collaborative community, we need to consider not only the structure of a network but also the performance of knowledge collaboration among members within the community. Traditional community discovery approaches are not suitable to discover knowledge collaborative communities since most of them focus too much on the network topologies, and ignore some other important factors. In this paper, we propose two community discovery approaches, which can be used in different sizes of networks, and take more knowledge collaboration factors into account. Compared with some other existing approaches, the proposed approach can perform better in forming knowledge collaborative communities for multi-domain problem solving.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA00700121
• 書誌情報: Journal of information processing; 巻 22, 号 2, p. 243-252, 発行日 2014-04-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6652
• 出版者 / 言語: ja
• 出版者: 情報処理学会