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PLoS One. 2018 May 21;13(5):e0197260. doi: 10.1371/journal.pone.0197260. eCollection 2018.

Detecting trends in academic research from a citation network using network representation learning.

Author information

1
The Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.

Abstract

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.

PMID:
29782521
PMCID:
PMC5962067
DOI:
10.1371/journal.pone.0197260
[Indexed for MEDLINE]
Free PMC Article

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