The Relationship between the Network Structure and Countries of Highly-cited Papers in International Collaboration Network

Abstract

Abstract: This study describes the international collaboration of China based on bibliometric data of
internationally co-authored papers from web-of-science during 2007—2016, from which the international
collaboration network is generated. The global network properties are calculated, indicating the network
is not exactly the same as small-world model or scale-free model. The network structure properties of
countries, including degree centrality, closeness centrality, eigenvector centrality, constraint and tie strength,
are measured. The relationship between the network structure and highly cited papers is tested in negative
binominal regression model. The results of regression analysis are stated as follows: eigenvector centrality
is the most influential factor in promoting highly cited papers, and the next is tie strength; constraint
significantly hampers the output of highly cited papers, and the next is degree centrality; the effect of
closeness centrality is not significant.

Key words: International collaboration, Network structure, Highly cited papers, Negative binominal regression

Tu Jing, Li Yongzhou, Zhang Wenping. The Relationship between the Network Structure and Countries of Highly-cited Papers in International Collaboration Network[J]. Libraly Journal, 2019, 38(7): 69-75.

References

[ 1 ] Laband D N, Tollison R D. Intellectual collaboration [J].Journal of Political economy, 2000, 108(3): 632-662.
[ 2 ] Wilson J, Al E. Knowledge, networks and nations:global scientific collaboration in the 21st century [J].Royal Society, 2011(3): 5-6.
[ 3 ] Adams J. Collaborations: The rise of researchnetworks [J]. Nature, 2012, 490(7420): 335.
[ 4 ] Newman M E J. The structure and function ofcomplex networks [J]. SIAM Review, 2003, 45(2):167–256.
[ 5 ] Gazni A, Sugimoto C R, Didegah F. Mapping worldscientific collaboration: Authors, institutions, andcountries [J]. Journal of the American Society forInformation Science & Technology, 2012, 64(12):323-335.
[ 6 ] Choi S, Yang J S, Park H W. The triple helix andinternational collaboration in science [J]. Journalof the Association for Information Science andTechnology, 2015, 66(1): 201-212.
[ 7 ] 刘承良, 桂钦昌, 段德忠, 等. 全球科研论文合作网络的结构异质性及其邻近性机理[J]. 地理学报, 2017, 72(4): 737-752.
[ 8 ] Wagner C S, Whetsell T A, Leydesdorff L. Growth ofinternational collaboration in science: revisiting sixspecialties [J]. Scientometrics, 2017, 110(3): 1633-1652.
[ 9 ] Basu A, Aggarwal R. International Collaborationin Science in India and its Impact on InstitutionalPerformance [J]. Scientometrics, 2001, 52(3):379-394.
[10] Gl?nzel W. National characteristics in internationalscientific co-authorship relations [J]. Scientometrics,2001, 51(1): 69-115.
[11] Bornmann L, Wagner C, Leydesdorff L. BRICScountries and scientific excellence: A bibliometricanalysis of most frequently cited papers [J]. Journalof the Association for Information Science &Technology, 2015, 66(7): 1507-1513.
[12] 李根.TOP 10研究前沿中高被引学术论文特征分析及启示——基于ESI数据库[J]. 编辑学报, 2018,30(2): 178-181.
[13] 何汶, 刘颖, 杨红梅. 图书情报学高被引论文国际合著及贡献研究——基于文献计量学角度[J]. 图书情报知识, 2017(2): 83-93.
[14] 刘多, 宋敏, 谢亚南, 等. 2009—2015年国家自然科学基金资助产出ESI高被引论文分析[J]. 中国科学基金, 2017(4): 353-358.
[15] Aksnes D W. Characteristics of highly cited papers [J].Research Evaluation, 2012, 12(3): 159-170.
[16] Newman M E J. Assortative Mixing in Networks [J].Physical Review Letters, 2002, 89(20): 208-701.
[17] Freeman L C. Centrality in social networksconceptual clarification [J]. Social Networks, 2008,1(3): 215-239.
[18] Bonacich P. Power and Centrality: A Family ofMeasures [J]. American Journal of Sociology, 1987,92(5): 1170-1182.
[19] Burt R S. Structural Holes and Good Ideas [J].American Journal of Sociology, 2013, 110(2):349-399.
[20] Granovetter M S. The strength of weak ties [J].American Journal of Sociology, 1973, 78(6): 1360.
[21] Levin D Z, Cross R. The Strength of Weak Ties YouCan Trust: The Mediating Role of Trust in EffectiveKnowledge Transfer [J]. Management Science, 2004,50(11): 1477-1490

[1]	Li Xin, Cheng Qikai. Research on the Recognition of Highly Cited Papers Based on “Precision-recall” Analysis#br# [J]. Libraly Journal, 2019, 38(1): 83-90.
[2]	Li Feng. Medium and Low Frequency Keywords in Co-word Analysis: A Case Study of Highly Cited Articles in Library and Information Science [J]. Libraly Journal, 2018, 37(4): 34-42.
[3]	Zhao Rongying,Wu Shengnan. Study on Network Structure and Pattern of Knowledge Transfer in Library and Information Science Journals: Based on Journal Cross-Citation Network [J]. LIBRARY JOURNAL, 2014, 33(8): 29-36.