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Analyzing Academic Hyperlink Structures and Co-Authorship Patterns in South Korean Universities

This study investigates the structural characteristics of academic hyperlinks among universities in South Korea and explores their relationship with research productivity. Using Social Network Analysis (SNA), we analyze hyperlink activities to infer communication patterns among universities. Key findings reveal significant differences in the number of published journal articles based on hyperlink connectivity. Grouping universities through CONCOR and visualizing relationships further illuminates how hyperlinking activity correlates with SCI journal publications, suggesting a complex interplay between connectivity and academic output.

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Analyzing Academic Hyperlink Structures and Co-Authorship Patterns in South Korean Universities

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  1. Group 1: 5th presentation Comparing academic hyperlink structure with co-authorship pattern in Korea Hyo Kim @ Ajou University Han Woo Park @ YeungNam University

  2. Hyo Kim College of Information Technology Media division Ajou University Korea (South) Tel) +82-31-219-1858 E-mail) hkimscil@commres.org

  3. Han Woo Park School of Social Sciences Yeung Nam University Korea (South) hanpark@yumail.ac.kr http://www.hanpark.net

  4. Study • Structural characteristics of academic hyperlinks among universities in Korea • Relationship between hyperlinks and productiveness • Speculation of actual communication patterns from the hyperlink activities • Via SNA (social network analysis) approach

  5. Data I • http://www.braintrack.com/ • The most visible two universities in each local region in Korea (South part, N = 30) • http://altavista.com/ • Number of in-links and out-links to the universities in the data set • ISI database • the number of research articles listed in the SCI index published in each university • 2 univ. dropped (N = 28)

  6. Data II – # of hyperlinks

  7. Data III

  8. Dichotomization for CONCOR • From the initial matrix (Data II) • Replacing binary values • Average of the matrix = 17.11 • Cells bellow the mean = 0 • Cells greater than or equal to (GE) the mean = 1 • New data matrix (see next page)

  9. CONCOR - dichotomized

  10. Groups identified by CONCOR

  11. MDS graph Groups Identified (from CONCOR)

  12. Relationships among the groups

  13. 1 A B .31 .79 .91 D .93 C .53 Visualization of group rel. • Group A, B, C, D (identified from CONCOR) can be visualized

  14. Group rel. • Members in group A = the strongest rel (regarding hyperlinks, value = 1) • Members in group C = strong rel (value = .53) • Strong rel between group A and C (A->C = .93; C -> A = .79) • Members in group B = isolated • Members in group D = no strong hyperlink activity among themselves, but, strong hyperlink-receivers (value = .91) and weak hyperlink maker (value = .31)

  15. ANOVA • Are these groups meaningful in terms of the number of links (in and out); and the number of articles? • In-links: F (3, 24) = 9.73, p < .0001 • Out-links: F (3, 24) = 62.79, p < .0001 • Articles: F (3, 24) = 8.26, p < .0001 • Group A differs from all other universities in terms of the number of published journal articles, which means the members of group A are strong research universities.

  16. QAP (dyadic rel) • CONCOR test just reveals general relationships among members in each group or among groups. • ANOVA test does not reveal relationships. • QAP will reveal specific relationship between universities and SCI articles at a dyadic level.

  17. QAP test • IV: in- and out-links matrices • DV: matrix of the number of articles

  18. DV = SCI journal articles • The number of SCI journal articles • The data set is not usable for QAP test because it is an attribute data (just one raw, it has). • So, the data is transformed into matrix (via obtaining the dyadic difference of the number of articles between two universities)

  19. DV = SCI journal articles • Each number in a cell means the absolute difference (of the number of articles) between two universities

  20. QAP result • R-square = 35.6% • Both In and Out matrix are significantly related to the DV matrix (# of articles; In = .41; Out = .24), which means . . . • at a dyadic level, if one university has more links (both in and out), the university produces more SCI journal articles. • Caution: a kind of regression test, which means • no causal relationship between IVs and DV are assumed. • Therefore, we can just speculated that SCI journals are significantly related to number of links.

  21. Study discussion • With SNA, we explored • At structural level • The structural characteristic of the whole matrix of in and out hyperlinks. • Four groups identified from the structural characteristics • Four groups differed from # of SCI articles, which means hyperlinking activity is related to the journal publication.

  22. Study Discussion II • At a dyadic level, • Specifically, # of SCI articles is related to # of in and out links between two universities.

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