Title

Mining Diversity on Networks

Publication Type

Conference Proceeding Article

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

Research Areas

Data Management and Analytics

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

Tsukuba, Japan

Additional URL

http://dx.doi.org/10.1007/978-3-642-12026-8_30