Year-end Sale % OFF 
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.
Highlights
A large part of technological development in our society is based on academic research [1, 2]
As described in this paper, we propose a framework that detects the growth direction of a network, which is considered to indicate a trend
Using the American Physical Society (APS) dataset as well as data from certain domains of the Web of Science, our framework detects research trends and confirms that a paper located in a cutting edge area is likely to be highly cited in the future
Summary
A large part of technological development in our society is based on academic research [1, 2]. From a different approach, producing a geometric graph model [16] provides information related to the classification of citations and citation diversity of each paper These methods cannot elucidate the growth direction of the whole network. In the iterative node addition process in the citation network, the direction from a new node to existing nodes is affected by the direction from nodes which the node cites existing nodes During this repetition, we assume that the citation network grows into specific directions in latent space obtained by LINE. From APS paper citation datasets or papers of some domains of the Web of Science, we confirmed the existence of trends by observing linear growth of the network in latent space. Papers located in the cutting-edge, the vanguard of the growth direction of the network on latent space, will be cited many times. The information is valid for predicting growth of the academic field
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
