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

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 linearl

SUBMITTER: Asatani K 

PROVIDER: S-EPMC5962067 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature