IRIS: Learning the Underlying Information of Scientific Research Interests Using Heterogeneous Network Representation

Date
2022-01-04
Authors
Feng, Zihan
Cui, Hongfei
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Understanding scientific research fields and finding potential relations between seemingly distinct fields can help researchers rapidly grasp their most interested topics with expertises. In this study, we construct a heterogeneous network which contains authors, keywords, papers and institutions, and built an “Integrated Research Interest Space (IRIS)” which can represent both author and keyword nodes. Similar keywords in the sense of research interest and research manner can obvious aggregate together. Authors that are interested in different keywords distributed in different IRIS areas, with strongly associated with research objectives and methodologies of the keywords. The average similarities between authors and their real used keywords is significantly higher than that of randomly chosen author-keyword pairs. Based on these observations, we propose a simple algorithm which attempts to recommend potential interested keywords for researchers, and got meaningful results. Our study may also give useful hints for understanding research interests and discovering potential cross disciplines.
Description
Keywords
Big Data-driven Social Media Management, heterogeneous network, network embedding, scientific research, visualization
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 55th Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.