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

12 Citations (Scopus)


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
Number of pages13
ISBN (Electronic)9783030367084
ISBN (Print)9783030367077
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
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029


Conference26th International Conference on Neural Information Processing
Abbreviated titleICONIP 2019

Bibliographical note

This work was supported by National Key R&D Program of China (Grant No. 2017YF-C0804003), National Natural Science Foundation of China (Grant No. 61976111), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant Nos. JCYJ20170817112421757 and JCYJ20180504165652917) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).


  • Heterogeneous information networks
  • Network analysis
  • Network embedding


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