Mining diversity on networks

Lu Liu, Feida Zhu, Chen Chen, Xifeng Yan, Jiawei Han, Gabrielle Dawn Allen, Shiqiang Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationDatabase Systems for Advanced Applications - 15th International Conference, DASFAA 2010, Proceedings
Pages384-398
Number of pages15
EditionPART 1
DOIs
StatePublished - 2010
Event15th International Conference on Database Systems for Advanced Applications, DASFAA 2010 - Tsukuba, Japan
Duration: Apr 1 2010Apr 4 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5981 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Database Systems for Advanced Applications, DASFAA 2010
Country/TerritoryJapan
CityTsukuba
Period4/1/104/4/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Mining diversity on networks'. Together they form a unique fingerprint.

Cite this