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Representation Learning on Graphs - A Survey

EasyChair Preprint no. 4583

7 pagesDate: November 16, 2020


Learning methods to represent graph nodes as feature vectors is a field that has recently seen a surge in research. Embedding graph nodes as vectors is useful to make graph datasets suitable for use in several downstream machine learning tasks. In this survey, we attempt to present an overview of the various methods found in the literature.

Keyphrases: graphs, node embeddings, Representation Learning, survey

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ankur Sharma and Mehak Preet Dhaliwal and Kartikeya Sharma},
  title = {Representation Learning on Graphs - A Survey},
  howpublished = {EasyChair Preprint no. 4583},

  year = {EasyChair, 2020}}
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