Interaction content aware network embedding via co-embedding of nodes and edges

Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Network embedding has been a hot topic as it can learn node representations that encode the network structure resulting from node interactions. In this paper, besides the network structure, the interaction content within which each interaction arises is also embedded because it reveals interaction preferences of the two nodes involved. Specifically, we propose interaction content aware network embedding (ICANE) via co-embedding of nodes and edges. The embedding of edges is to learn edge representations that preserve the interaction content, which then can be incorporated into node representations through edge representations. Experiments demonstrate ICANE outperforms five recent network embedding models in visualization, link prediction and classification.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
EditorsBao Ho, Dinh Phung, Geoffrey I. Webb, Vincent S. Tseng, Mohadeseh Ganji, Lida Rashidi
PublisherSpringer-Verlag
Pages183-195
Number of pages13
ISBN (Print)9783319930367
DOIs
Publication statusPublished - 1 Jan 2018
Event22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10938 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
Country/TerritoryAustralia
CityMelbourne
Period3/06/186/06/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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