Citation prediction in heterogeneous bibliographic networks

Xiao Yu, Quanquan Gu, Mianwei Zhou, Jiawei Han

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

Abstract

To reveal information hiding in link space of biblio- graphical networks, link analysis has been studied from different perspectives in recent years. In this paper, we address a novel problem namely citation prediction, that is: given information about authors, topics, target publication venues as well as time of certain research paper, finding and predicting the citation relationship between a query paper and a set of previous papers. Considering the gigantic size of relevant papers, the loosely connected citation network structure as well as the highly skewed citation relation distribution, citation prediction is more challenging than other link prediction problems which have been studied before. By building a meta-path based prediction model on a topic discrim- inative search space, we here propose a two-phase cita- Tion probability learning approach, in order to predict citation relationship effectively and efficiently. Exper- iments are performed on real-world dataset with com- prehensive measurements, which demonstrate that our framework has substantial advantages over commonly used link prediction approaches in predicting citation relations in bibliographical networks.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
PublisherSociety for Industrial and Applied Mathematics Publications
Pages1119-1130
Number of pages12
ISBN (Print)9781611972320
DOIs
StatePublished - 2012
Event12th SIAM International Conference on Data Mining, SDM 2012 - Anaheim, CA, United States
Duration: Apr 26 2012Apr 28 2012

Publication series

NameProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Other

Other12th SIAM International Conference on Data Mining, SDM 2012
Country/TerritoryUnited States
CityAnaheim, CA
Period4/26/124/28/12

ASJC Scopus subject areas

  • Computer Science Applications

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