A capsule network-based model for learning node embeddings

Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung

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Abstract

In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE - a new unsupervised embedding model leveraging a network of two capsule layers. Caps2NE induces a routing process to aggregate feature vectors of context neighbors of a given target node at the first capsule layer, then feed these features into the second capsule layer to infer a plausible embedding for the target node. Experimental results show that our proposed Caps2NE obtains state-of-the-art performances on benchmark datasets for the node classification task. Our code is available at: https://github.com/daiquocnguyen/Caps2NE.

Original languageEnglish
Title of host publicationProceedings of the 29th ACM International Conference on Information & Knowledge Management
EditorsClaudia Hauff, Edward Curry, Philippe Cudre Mauroux
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages3313-3316
Number of pages4
ISBN (Electronic)9781450368599
DOIs
Publication statusPublished - 2020
EventACM International Conference on Information and Knowledge Management 2020 - Virtual, Online, Ireland
Duration: 19 Oct 202023 Oct 2020
Conference number: CIKM 2020
https://dl.acm.org/doi/proceedings/10.1145/3340531 (Proceedings)
https://www.cikm2020.org/ (Website)

Conference

ConferenceACM International Conference on Information and Knowledge Management 2020
Abbreviated title29th
CountryIreland
CityVirtual, Online
Period19/10/2023/10/20
Internet address

Keywords

  • capsule networks
  • graph representation learning
  • node classification
  • node embeddings

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