Skip to Main content Skip to Navigation
Conference papers

On Joint Representation Learning of Network Structure and Document Content

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.
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Monday, January 8, 2018 - 9:49:51 AM
Last modification on : Wednesday, March 28, 2018 - 4:35:04 PM
Long-term archiving on: : Friday, May 4, 2018 - 7:47:18 AM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Jörg Schlötterer, Christin Seifert, Michael Granitzer. On Joint Representation Learning of Network Structure and Document Content. 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2017, Reggio, Italy. pp.237-251, ⟨10.1007/978-3-319-66808-6_16⟩. ⟨hal-01677137⟩



Record views


Files downloads