International Journal of Economics, Finance and Management Sciences
Volume 5, Issue 5, October 2017, Pages: 268-275
Received: Oct. 23, 2017;
Published: Oct. 27, 2017
Views 777 Downloads 42
Jianhua Wang, School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China
Hong Li, School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China
To date, very few research has been taken to quantitatively visualize the intellectual evolution of the field of detecting research fronts. Based on literature-searching analysis from the Thomson Reuters Web of Science as well as scientometrics mapping analysis, this important research topic is analyzed by techniques from informetric and bibliometric domains to detect its intellectual structures and dynamics. After data reduction and clean-up, 1841 articles published from 1991 to 2016 are downloaded, and two network analyses are conducted: a bibliometric approach (i.e. co-occurrence and co-citation network) and a complex network approach utilizing Chen’s CiteSpace. Results are illustrated as the following: (1) growth of publications is divided into three stages: a stagnant phase (1991 - 2002), a takeoff phase (2003 - 2009) and a blooming phase (2010 - 2016); (2) most of the research institutions in this domain are concentrated in the United States, China, Japan and some developed countries in Europe. Chinese research in this area is mainly concentrated in Wuhan university, Chinese academy of science and Dalian university of technology; (3) publications of detecting research fronts mainly focus on the disciplines of Compute Science, Engineering and Information science library science; (4) we find three main academic communities in this domain, and they mainly focus on development and application of methods detecting research fronts; (5) methods detecting research fronts are classified into two categories: citations analysis and topic words analysis; (6) Scientific researchers began to frequently use the database for scientometrics research after 2012 and medical field began to use this approach to understand some of the frontier dynamics in the field in 2014.
Mapping Intellectual Structures and Dynamics of Detecting Research Fronts Domain, International Journal of Economics, Finance and Management Sciences.
Vol. 5, No. 5,
2017, pp. 268-275.
D. D. Price, Networks of Scientific Papers, Science 149 (1965) 510–515.
Small, Co-citation in the Scientific Literature: A New Measure of the Relationship Between Two Documents, Journal of the American Society for Information Science 24(4) (1973) 265-269.
Griffith, The Structure of Scientific Literatures II: Toward a Macro- and Microstructure for Science, Social Studies of Science 4(4) (1974) 339-365.
Persson, The Intellectual Base and Research Fronts of JASIS 1986-1990, Journal of the American Society for Information Science 45(1) (1994) 31-38.
Morris, Time Line Visualization of Research Fronts, Journal of the American Society for Information Science and Technology 54(5) (2003) 413-422.
Braam, Mapping of Science by Combined Co-Citation and Word Analysis. I. Structural Aspects, Journal of the American Society for Information Science and Techology 42(4) (1991) 233-251.
C. Chen, CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature, Journal of the American Society for Information Science and Technology 57(3) (2006) 359-377.
C. Liu, Q. Gui, Mapping intellectual structures and dynamics of transport geography research: a scientometric overview from 1982 to 2014, Scientometrics 109(1) (2016) 159-184.
M. J. Cobo, A. G. López-Herrera, E. Herrera-Viedma, F. Herrera, Science mapping software tools: Review, analysis, and cooperative study among tools, Journal of the American Society for Information Science and Technology 62(7) (2011) 1382-1402.
C. Chen, Searching for intellectual turning points: Progressive Know ledge Domain Visualization, Proceedings of the National Academy of Sciences of the United States of America 101 (2004) 5303-5310.
C. Chen, F. Ibekwe-SanJuan, J. Hou, The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis, Journal of the American Society for Information Science and Technology 61(7) (2010) 1386-1409.
L. An, X. Lin, C. Yu, X. Zhang, Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes, Scientometrics 105(3) (2015) 1605-1634.
H. Yang, W.-S. Jung, Structural dynamics of keyword networks: Liquid crystal display and plasma display panel cases, Journal of Engineering and Technology Management 40 (2016) 64-75.
A. Abdallah, M. A. Maarof, A. Zainal, Fraud detection system: A survey, Journal of Network and Computer Applications 68 (2016) 90-113.
H. Wang, W. He, F.-K. Wang, Enterprise cloud service architectures, Information Technology and Management 13(4) (2012) 445-454.
H. Small, The Structure of Scientific Literatures I: Identifying and Graphing Specialties, Science Studies 4(1) (1974) 17-40.
L. i. S. Kernighan An Efficient Heuristic Procedure for Partitioning Graphs Bell System Technical Journal 49(2) (1970) 291-307.
S. H. Pothen A, PARTITIONING SPARSE MATRICES WITH EIGENVECTORS OF GRAPHS, SIAM J Matrix AnalAppl 11(3) (1990) 430-452.
M. Girvan, Newman., Community structure in social and biological networks Proc. Natl. Acad. Sci 99 (2002) 7821-7826.
Newman, Fast algorithm for detecting community structure in networks, Phys Rev E 69(6) (2004) 066133.
K. J, Bursty and Hierarchical Structure in Streams, Data Mining and Knowledge Discovery 7(4) (2003) 373-397.
Q. Wu, C. Zhang, X. An, Topic segmentation model based on ATNLDA and co-occurrence theory and its application in stem cell field, Journal of Information Science 39(3) (2012) 319-332.