Relationship emergence prediction in heterogeneous networks through dynamic frequent subgraph mining

Yang Liu, Songhua Xu, Lian Duan

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

4 Scopus citations

Abstract

With the rapid development of Web 2.0 and the Internet of things, predicting relationships in heterogeneous networks has evolved as a heated research topic. Traditionally, people analyze existing relationships in heterogeneous networks that relate in a particular way to a target relationship of interest to predict the emergence of the target relationship. However most existing methods are incapable of systematically identifying relevant relationships useful for the prediction task, especially those relationships involving multiple objects of heterogeneous types, which may not rest on a simple path in the concerned heterogeneous network. Another problem with the current practice is that the existing solutions often ignore the dynamic evolution of the network structure after the introduction of newly emerged relationships. To overcome the first limitation, we propose a new algorithm that can systematically and comprehensively detect relevant relationships useful for the prediction of an arbitrarily given target relationship through a disciplined graph searching process. To address the second limitation, the new algorithm leverages a series of temporally-sensitive features for the relationship occurrence prediction via a supervised learning approach. To explore the effectiveness of the new algorithm, we apply the prototype implementation of the algorithm on the DBLP bibliographic network to predict the author citation relationships and compare the algorithm performance with that of a state-of-the-art peer method and a series of baseline methods. The comparison shows consistently higher prediction accuracy under a range of prediction scenarios.

Original languageEnglish (US)
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages1649-1658
Number of pages10
ISBN (Electronic)9781450325981
DOIs
StatePublished - Nov 3 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: Nov 3 2014Nov 7 2014

Publication series

NameCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
CountryChina
CityShanghai
Period11/3/1411/7/14

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Computer Science Applications
  • Information Systems

Keywords

  • Heterogeneous network
  • Relationship prediction

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