Paper Info
Title
More Complex More Productive: Characterizing Top Universities Based on Research Publications
Abstract
Exploring new scientific concepts and imparting knowledge are important roles of universities. Up to now, most information management study on institutional research output focuses on the number and excellence of paper. This paper proposes a new characterization method from the perspective of output and complexity to extract academic information. Top-ranked universities are selected to identify different performance through research production and complexity. The production indicator of different universities is calculated based on the annual number of research paper produced in each university. The complexity indicator of different universities is obtained according to weighted revealed comparative advantage over different research disciplines. By using an unsupervised competitive learning algorithm that considers four indicators simultaneously, we construct a coherent framework to seize the nature of universities' research output. As a key finding, we discover that university research complexity has a positive relationship with research production and a different cluster of universities has a different rate of rising of the positive relationship.
Year
DOI
Venue
2021
10.1109/IMCOM51814.2021.9377359
2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)
Keywords
DocType
ISSN
Academic data mining,Unsupervised learning,Complexity modeling,Self Organizing Map
Conference
2644-0164
ISBN
Citations 
PageRank 
978-1-6654-4619-8
0
0.34
References 
Authors
17
6
Name
Order
Citations
PageRank
Jiaxing Li100.68
Luna Wang200.34
Yiming Sun300.68
Guojiang Shen48613.23
Ivan Lee520.71
Xiangjie Kong642546.56