Paper Info
Title
Dsrs: Estimation And Forecasting Of Journal Influence In The Science And Technology Domain Via A Lightweight Quantitative Approach
Abstract
The evaluation of journals based on their influence is of interest for numerous reasons. Various methods of computing a score have been proposed for measuring the scientific influence of scholarly journals. Typically the computation of any of these scores involves compiling the citation information pertaining to the journal under consideration. This involves significant overhead since the article citation information of not only the journal under consideration but also that of other journals for the recent few years need to be stored. Our work is motivated by the idea of developing a computationally lightweight approach that does not require any data storage, yet yields a score which is useful for measuring the importance of journals. In this paper, a regression analysis based method is proposed to calculate Journal Influence Score. Proposed model is validated using historical data from the SCImago portal. The results show that the error is small between rankings obtained using the proposed method and the SCImago Journal Rank, thus proving that the proposed approach is a feasible and effective method of calculating scientific impact of journals.
Year
DOI
Venue
2016
10.1080/09737766.2016.1177939
COLLNET JOURNAL OF SCIENTOMETRICS AND INFORMATION MANAGEMENT
Keywords
DocType
Volume
Journal Influence Score (JIS), Downselection with Regression and Significance scheme (DSRS), Multiple Linear Regression (MLR), Clustering, Significance test, Internationality, Principal representative features
Journal
10
Issue
ISSN
Citations 
1
0973-7766
2
PageRank 
References 
Authors
0.55
7
4
Name
Order
Citations
PageRank
Snehanshu Saha14617.96
Neelam Jangid241.32
Archana Mathur383.56
M. N. Anand420.89