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

Kimitaka Asatani1, Junichiro Mori1, Masanao Ochi1

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

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Summary

This study introduces a new framework using network representation learning (NRL) to identify academic trends by analyzing citation network growth. The method predicts future citations and identifies emerging research areas.

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Area of Science:

  • Bibliometrics
  • Network Science
  • Information Science

Background:

  • Citation networks are crucial for understanding scientific impact but struggle to reveal field trends or cutting-edge research.
  • Existing methods for analyzing citation networks lack the ability to dynamically track evolving research landscapes.

Purpose of the Study:

  • To develop a novel framework for detecting trends in academic fields by analyzing citation network growth.
  • To leverage network representation learning (NRL) to model the directional evolution of citation networks.
  • To introduce a metric for quantifying a publication's trend-following behavior and its predictive power.

Main Methods:

  • Proposed a framework utilizing network representation learning (NRL) to model citation network dynamics.
  • Assumed linear growth in latent space represents the iterative process of citation network formation.
  • Validated the framework on APS and Web of Science datasets, observing directional growth in academic fields.

Main Results:

  • Confirmed that academic fields exhibit linear growth in a specific direction within the latent space.
  • Introduced the intrinsic publication year (IPY) as an indicator of trend-following, showing correlation with future citations.
  • Found that frequently used words in high-IPY papers are likely to appear in future publications, validating trend detection.

Conclusions:

  • The proposed NRL-based framework effectively detects trends in academic fields by analyzing citation network growth.
  • The intrinsic publication year (IPY) serves as a valid indicator for predicting citation network evolution and identifying emerging research.
  • This approach offers a powerful tool for understanding and forecasting the development of scientific domains.