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Measuring the Impact of Co-Author Count on Citation Count of Research Publications

by Wael Alharbi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 52
Year of Publication: 2022
Authors: Wael Alharbi
10.5120/ijca2022921935

Wael Alharbi . Measuring the Impact of Co-Author Count on Citation Count of Research Publications. International Journal of Computer Applications. 183, 52 ( Feb 2022), 10-17. DOI=10.5120/ijca2022921935

@article{ 10.5120/ijca2022921935,
author = { Wael Alharbi },
title = { Measuring the Impact of Co-Author Count on Citation Count of Research Publications },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 52 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 10-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number52/32280-2022921935/ },
doi = { 10.5120/ijca2022921935 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:12.029615+05:30
%A Wael Alharbi
%T Measuring the Impact of Co-Author Count on Citation Count of Research Publications
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 52
%P 10-17
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Citations of any work is considered as a major trait that leads to the work evaluation and investigation. Citations is one of the major measures to access the quality of the research publication. Citations can have positive or negative impact on any piece of work or publication through many different factors, such as author expertise level, publication venue, topic that is researched etc. This research aims at investigating how co-author count impact the citations of the research publications. There will be a correlation analysis between co-author count and citation of research publications. In this paper, Citation Network Dataset is used. The data set is designed for research purpose. The citation data is extracted from DBLP, ACM, MAG (Microsoft Academic Graph), and other sources. The first version contains 629,814 papers and 632,752 citations. To test the impact of co-author count on citation count of a research publications, two methods are illustrated: (i) Pearson’s Correlation Coefficient (PCC), and (ii) Multiple Regression (MR). To test the impact of co-author count on citation count of a research publications, two methods are illustrated: (i) Pearson’s Correlation Coefficient Calculation (PCC), and (ii) Multiple Regression (MR). To test the impact of co-author count on citation count of research publications, Pearson’s correlation coefficient (ra) between the two variables Number of Authors (NA) and Citation Count (CC) is calculated. Pearson’s correlation coefficient between the Citation Count (CC) and the most effective variables to compare between the impact of the number of authors and the impact of the other factors is calculated such as: (i) rc between the two variables Number of Countries (NC) and Citation Count (CC). (ii) rv between Venue Category (VC) and Citation Count (CC). (iii) ry between Year_From (YF) and Citation Count (CC). (iv) rp between the two variables Publisher (P) and Citation Count (CC). (v) rr between the two variables Number_of_references (R) and Citation Count (CC). (vi) rs between the two variables Paper_size (S) and Citation Count (CC). Empirical evidence shows that co-authored publications achieve higher visibility and impact. In order to predict the number of citations from the previous mentioned factors (NA, NC, VC, YF, P, R, S), we use Multiple Linear Regression (MLR). The goal of multiple linear regression (MLR) is to model the linear relationship between the explanatory (independent) variables and response (dependent) variable. The higher R-square, the tight relationship exists between dependent variables and independent variables. It is observed that the R-square decreases in case of removing NA which means that the NA is the most influential factor.

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Index Terms

Computer Science
Information Sciences

Keywords

Citation analysis Research collaboration co-authorship Indexing Publication venues Academic libraries Entropy

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