Detecting trends in academic research from a citation network using network representation learning.
Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation netw...
Published in: | PLOS ONE |
---|---|
Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Public Library of Science (PLoS)
2018
|
Subjects: | |
Online Access: | https://doi.org/10.1371/journal.pone.0197260 https://doaj.org/article/4a95534329d1483cb00619074d09e417 |
id |
ftdoajarticles:oai:doaj.org/article:4a95534329d1483cb00619074d09e417 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:4a95534329d1483cb00619074d09e417 2023-05-15T16:55:49+02:00 Detecting trends in academic research from a citation network using network representation learning. Kimitaka Asatani Junichiro Mori Masanao Ochi Ichiro Sakata 2018-01-01T00:00:00Z https://doi.org/10.1371/journal.pone.0197260 https://doaj.org/article/4a95534329d1483cb00619074d09e417 EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC5962067?pdf=render https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0197260 https://doaj.org/article/4a95534329d1483cb00619074d09e417 PLoS ONE, Vol 13, Iss 5, p e0197260 (2018) Medicine R Science Q article 2018 ftdoajarticles https://doi.org/10.1371/journal.pone.0197260 2022-12-31T00:55:38Z Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node's degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. Article in Journal/Newspaper IPY Directory of Open Access Journals: DOAJ Articles PLOS ONE 13 5 e0197260 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Medicine R Science Q |
spellingShingle |
Medicine R Science Q Kimitaka Asatani Junichiro Mori Masanao Ochi Ichiro Sakata Detecting trends in academic research from a citation network using network representation learning. |
topic_facet |
Medicine R Science Q |
description |
Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node's degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. |
format |
Article in Journal/Newspaper |
author |
Kimitaka Asatani Junichiro Mori Masanao Ochi Ichiro Sakata |
author_facet |
Kimitaka Asatani Junichiro Mori Masanao Ochi Ichiro Sakata |
author_sort |
Kimitaka Asatani |
title |
Detecting trends in academic research from a citation network using network representation learning. |
title_short |
Detecting trends in academic research from a citation network using network representation learning. |
title_full |
Detecting trends in academic research from a citation network using network representation learning. |
title_fullStr |
Detecting trends in academic research from a citation network using network representation learning. |
title_full_unstemmed |
Detecting trends in academic research from a citation network using network representation learning. |
title_sort |
detecting trends in academic research from a citation network using network representation learning. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2018 |
url |
https://doi.org/10.1371/journal.pone.0197260 https://doaj.org/article/4a95534329d1483cb00619074d09e417 |
genre |
IPY |
genre_facet |
IPY |
op_source |
PLoS ONE, Vol 13, Iss 5, p e0197260 (2018) |
op_relation |
http://europepmc.org/articles/PMC5962067?pdf=render https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0197260 https://doaj.org/article/4a95534329d1483cb00619074d09e417 |
op_doi |
https://doi.org/10.1371/journal.pone.0197260 |
container_title |
PLOS ONE |
container_volume |
13 |
container_issue |
5 |
container_start_page |
e0197260 |
_version_ |
1766046874511343616 |