Department of Technology Management for Innovation, The University of Tokyo, Bunkyo, Tokyo, Japan.
Human Informatics Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan.
PLoS One. 2022 Sep 14;17(9):e0274253. doi: 10.1371/journal.pone.0274253. eCollection 2022.
Identifying promising research as early as possible is vital to determine which research deserves investment. Additionally, developing a technology for automatically predicting future research trends is necessary because of increasing digital publications and research fragmentation. In previous studies, many researchers have performed the prediction of scientific indices using specially designed features for each index. However, this does not capture real research trends. It is necessary to develop a more integrated method to capture actual research trends from various directions. Recent deep learning technology integrates different individual models and makes it easier to construct more general-purpose models. The purpose of this paper is to show the possibility of integrating multiple prediction models for scientific indices by network-based representation learning. This paper will conduct predictive analysis of multiple future scientific impacts by embedding a heterogeneous network and showing that a network embedding method is a promising tool for capturing and expressing scientific trends. Experimental results show that the multiple heterogeneous network embedding improved 1.6 points than a single citation network embedding. Experimental results show better results than baseline for the number of indices, including the author h-index, the journal impact factor (JIF), and the Nature Index after three years from publication. These results suggest that distributed representations of a heterogeneous network for scientific papers are the basis for the automatic prediction of scientific trends.
尽早识别有前途的研究对于确定哪些研究值得投资至关重要。此外，由于数字出版物的增加和研究的碎片化，开发一种自动预测未来研究趋势的技术也是必要的。在以前的研究中，许多研究人员使用为每个指标专门设计的特征来进行科学指标的预测。然而，这并不能捕捉到真正的研究趋势。有必要开发一种更综合的方法，从各个方向捕捉实际的研究趋势。最近的深度学习技术集成了不同的个体模型，使得构建更通用的模型变得更加容易。本文旨在通过基于网络的表示学习展示整合多个科学指标预测模型的可能性。本文通过嵌入异构网络对多个未来科学影响进行预测分析，表明网络嵌入方法是捕捉和表达科学趋势的有前途的工具。实验结果表明，与单一引文网络嵌入相比，多个异构网络嵌入提高了 1.6 个点。实验结果表明，与基线相比，包括出版物后三年的作者 h 指数、期刊影响因子 (JIF) 和自然指数在内的多个指标的结果更好。这些结果表明，科学论文异构网络的分布式表示是科学趋势自动预测的基础。
