Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2010
Abstract
Despite the recent emergence of many large-scale networks in different application domains, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. We first lay out two criteria that capture the semantic meaning of diversity, and then propose a compliant definition which is simple enough to embed the idea. An efficient top-k diversity ranking algorithm is developed for computation on dynamic networks. Experiments on both synthetic and real datasets give interesting results, where individual nodes identified with high diversities are intuitive.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part I
Volume
5981
First Page
384
Last Page
398
ISBN
9783642120268
Identifier
10.1007/978-3-642-12026-8_30
Publisher
Springer Verlag
City or Country
Heidelberg
Citation
LIU, Lu; ZHU, Feida; CHEN, Chen; YAN, Xifeng; HAN, Jiawei; YU, Philip; and YANG, Shiqiang.
Mining Diversity on Networks. (2010). Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part I. 5981, 384-398.
Available at: https://ink.library.smu.edu.sg/sis_research/509
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-642-12026-8_30
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons