On Joint Representation Learning of Network Structure and Document Content

Jörg Schlötterer, Christin Seifert, Michael Granitzer

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
28 Downloads (Pure)

Abstract

Inspired by the advancements of representation learning for natural language processing, learning continuous feature representations of nodes in networks has recently gained attention. Similar to word embeddings, node embeddings have been shown to capture certain semantics of the network structure. Combining both research directions into a joint representation learning of network structure and document content seems a promising direction to increase the quality of the learned representations. However, research is typically focused on either word or network embeddings and few approaches that learn a joint representation have been proposed. We present an overview of that field, starting at word representations, moving over document and network node representations to joint representations. We make the connections between the different models explicit and introduce a novel model for learning a joint representation. We present different methods for the novel model and compare the presented approaches in an evaluation. This paper explains how the different models recently proposed in the literature relate to each other and compares their performance.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Extraction
Subtitle of host publicationFirst IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings
EditorsAndreas Holzinger, Peter Kieseberg, A. Min Tjoa, Edgar Weippl
Place of PublicationCham
PublisherSpringer
Pages237-251
ISBN (Electronic)978-3-319-66808-6
ISBN (Print)978-3-319-66807-9
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event1st International IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction 2017 - Reggio di Calabria, Italy
Duration: 29 Aug 20171 Sept 2017
Conference number: 1
https://cd-make.net/archive2017/call-for-papers/index.html

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume10410
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction 2017
Abbreviated titleCD-MAKE 2017
Country/TerritoryItaly
CityReggio di Calabria
Period29/08/171/09/17
Internet address

Keywords

  • n/a OA procedure

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