Prediction of Category of Scientific Article by Graph Convolution

Sachio Hirokawa, Takahiko Suzuki, Tetsuya Nakatoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The convolution method that uses the IDs of citing paper and cited paper is known to improve the prediction performance of category of papers. This paper proposes a "word convolution"method that uses not only the IDs of the cited and citing papers, but also the words that appear in those papers. The proposed method improves the prediction performance (accuracy) 7% for the core dataset and 12% for the citeseer dataset and gives the same performance for the pubmed dataset compared with the state-of-the-art method.

Original languageEnglish
Title of host publicationProceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
EditorsTokuro Matsuo, Kunihiko Takamatsu, Yuichi Ono, Sachio Hirokawa
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages63-67
Number of pages5
ISBN (Electronic)9781728173979
DOIs
Publication statusPublished - Sep 2020
Event9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020 - Kitakyushu, Japan
Duration: Sep 1 2020Sep 15 2020

Publication series

NameProceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

Conference

Conference9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
Country/TerritoryJapan
CityKitakyushu
Period9/1/209/15/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management

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