Publication detail

Graph neural network for website element detection

MYŠKA,V. BURGET, R. BREZANY, P.

Original Title

Graph neural network for website element detection

Type

conference paper

Language

English

Original Abstract

Websites are a mixture of structured HTML tags, unstructured natural language and styling, which gives a wide range of possibilities how a website can look like. The paper introduces a website node detector based on the so-called graph neural networks - a new kind of neural networks, which are not working just with tensors like traditional neural networks do, but operates with graphs (or tree structures - special variations of graphs). To assess the accuracy of the proposed methodology, a privately collected and labeled data set was created. Although the data set used for the experiment is relatively limited, results on this limited data set suggest, that this methodology may be a promising path for automatic content generation.

Keywords

graph neural network; deep learning; machine learning; node classification; mark-up languages

Authors

MYŠKA,V.; BURGET, R.; BREZANY, P.

Released

4. 7. 2019

Publisher

IEEE

Location

Budapest, Hungary

ISBN

978-1-7281-1864-2

Book

42nd International Conference on Telecommunications and Signal Processing (TSP)

Pages from

216

Pages to

219

Pages count

4

BibTex

@inproceedings{BUT157768,
  author="Vojtěch {Myška} and Radim {Burget} and Peter {Brezany}",
  title="Graph neural network for website element detection",
  booktitle="42nd International Conference on Telecommunications and Signal Processing (TSP)",
  year="2019",
  pages="216--219",
  publisher="IEEE",
  address="Budapest, Hungary",
  doi="10.1109/TSP.2019.8769036",
  isbn="978-1-7281-1864-2"
}