Plos One

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

Kimitaka Asatani ,Junichiro Mori,Masanao Ochi,Ichiro Sakata

2018

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.

特許出願やM&Aといった企業のイノベーションに関する活動もデータとして記録され蓄積されています。従来の経営学や経済学の研究で蓄積された様々なイノベーションに関する知見に、我々のデータサイエンスによる分析が俯瞰的かつデータドリブンな知見を上乗せできると考えています。例えば国内外のM&Aの大量データの分析により、企業の経営における投資の役割の理解が進みつつあります。また、取引ネットワークから地域や業界のハブとなる重要な企業の選出を行える手法の開発を進めています。