https://scholars.lib.ntu.edu.tw/handle/123456789/358048
標題: | Mining heterogeneous social networks for egocentric information abstraction | 作者: | Li, C.-T. Lin, S.-D. SHOU-DE LIN |
公開日期: | 2010 | 起(迄)頁: | 35-58 | 來源出版物: | From Sociology to Computing in Social Networks: Theory, Foundations and Applications | 摘要: | Social network is a powerful data structure that allows the depiction of relationship information between entities. However, real-world social networks are sometimes too complex for human to pursue further analysis. In this work, an unsupervised mechanism is proposed for egocentric information abstraction in heterogeneous social networks. To achieve this goal, we propose a vector space representation for heterogeneous social networks to identify combination of relations as features and compute statistical dependencies as feature values. These features, either linear or eyelie, intend to capture the semantic information in the surrounding environment of the ego. Then we design three abstraction measures to distill representative and important information to construct the abstracted graphs for visual presentation. The evaluations conducted on a real world movie datasct and an artificial crime dataset demonstrate that the abstractions can indeed retain significant information and facilitate more accurate and efficient human analysis. © 2010 Springer-Verlag Wien. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-84889954254&partnerID=MN8TOARS http://scholars.lib.ntu.edu.tw/handle/123456789/358048 |
DOI: | 10.1007/978-3-7091-0294-7_3 |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。