%0 Conference Proceedings
%T On Joint Representation Learning of Network Structure and Document Content
%+ University of Passau
%A Schlötterer, Jörg
%A Seifert, Christin
%A Granitzer, Michael
%Z Part 4: MAKE VIS
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Reggio, Italy
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-10410
%P 237-251
%8 2017-08-29
%D 2017
%R 10.1007/978-3-319-66808-6_16
%K Representation learning
%K Network embeddings
%K Document embeddings
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-01677137/document
%2 https://inria.hal.science/hal-01677137/file/456304_1_En_16_Chapter.pdf
%L hal-01677137
%U https://inria.hal.science/hal-01677137
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-LNCS-10410
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9