Representation learning for heterogeneous information networks via embedding events

Guoji FU, Bo YUAN, Qiqi DUAN, Xin YAO*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

16 Citations (Scopus)

Abstract

Network Representation Learning (NRL) has been widely used to analyze networks by mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackle this issue, this paper proposes a new NRL framework, called Event2vec, for HINs to consider both quantities and properties of relations during the representation learning process. Specifically, an event (i.e., a complete semantic unit) is used to represent the relation among multiple objects, and both event-driven first-order and second-order proximities are defined to measure the object relevance according to the quantities and properties of relations. We theoretically prove how event-driven proximities can be preserved in the embedding space by Event2vec, which utilizes event embeddings to facilitate learning the object embeddings. Experimental studies demonstrate its advantages over state-of-the-art algorithms on four real-world datasets and two network analysis tasks (i.e., link prediction and node classification). © Springer Nature Switzerland AG 2019.
Original languageEnglish
Title of host publicationNeural Information Processing : 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part I
EditorsTom GEDEON, Kok Wai WONG, Minho LEE
PublisherSpringer
Pages327-339
Number of pages13
ISBN (Electronic)9783030367084
ISBN (Print)9783030367077
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event26th International Conference on Neural Information Processing - Sydney, Australia
Duration: 12 Dec 201915 Dec 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11953
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
PublisherSpringer
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029

Conference

Conference26th International Conference on Neural Information Processing
Abbreviated titleICONIP 2019
Country/TerritoryAustralia
CitySydney
Period12/12/1915/12/19

Keywords

  • Heterogeneous information networks
  • Network analysis
  • Network embedding

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