Group 1: 5th presentation

Comparing academic hyperlink structure with co-authorship pattern in Korea

Hyo Kim@ Ajou University

Han Woo Park@ YeungNam University

Hyo Kim

College of Information TechnologyMedia divisionAjou UniversityKorea (South)

Tel) +82-31-219-1858E-mail) hkimscil@commres.org

Han Woo Park

School of Social SciencesYeung Nam University

Korea (South)hanpark@yumail.ac.kr

http://www.hanpark.net

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

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 S

CI index published in each university– 2 univ. dropped (N = 28)

Data II – # of hyperlinks

Universities - - - - - -

snu 139 86 31 67 …

korea 90 14 10 16 …

pusan 95 26 14 25 …

donga 20 8 9 9 …

kyungpook 36 13 11 1 …

keimyung 4 3 1 0 0 …

hannam 10 5 2 2 2 …

… … … … … … …

Data III

Indegree outdegree

snu 1224 1070

korea 324 471

pusan 393 373

donga 158 171

kyungpook 152 371

keimyung 27 137

inchon 56 69

chonnam 1088 287

chosun 104 182

… … …

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)

CONCOR - dichotomized

　 snu korea pusan dongaKyungpook …

snu 0 1 1 1 1 …

korea 1 0 0 0 0 …

pusan 1 1 0 0 1 …

donga 1 0 0 0 0 …

Kyungpook 1 0 0 0 0 …

… … … … … … …

Groups identified by CONCOR

MDS graph

GroupsIdentified(from CONCOR)

10-1

1

0

-1

snu

korea

pusan

donga

kyungpook

keimyung

hannam

cnu

inha

inchon

chonnam

chosun

cheju

hallym

kangwon

ajou

suwon

chungbuk

hoseo

schyeungnam

cataegu

gsnu

changwon

chonbukwonkwang

mokpo

daebul

A

B

C

D

10-1

1

0

-1

snu

korea

pusan

donga

kyungpook

keimyung

hannam

cnu

inha

inchon

chonnam

chosun

cheju

hallym

kangwon

ajou

suwon

chungbuk

hoseo

schyeungnam

cataegu

gsnu

changwon

chonbukwonkwang

mokpo

daebul

10-1

1

0

-1

snu

korea

pusan

donga

kyungpook

keimyung

hannam

cnu

inha

inchon

chonnam

chosun

cheju

hallym

kangwon

ajou

suwon

chungbuk

hoseo

schyeungnam

cataegu

gsnu

changwon

chonbukwonkwang

mokpo

daebul

A

B

C

D

Relationships among the groups

　 A B C D

A 1 0 0.93 0.91

B 0 0.33 0 0

C 0.79 0 0.53 0.089

D 0.31 0 0 0.06

Average 　 　 　 0.31

Visualization of group rel.

• Group A, B, C, D (identified from CONCOR) can be visualized

.31

.53

.79

.91

1

.93

AB

CD

Group rel.• Members in group A = the strongest rel (regardin

g 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)

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.

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.

QAP test

• IV: in- and out-links matrices• DV: matrix of the number of articles

DV = SCI journal articles

sci

snu 3828

korea 1193

pusan 757

donga 191

kyungpook 951

keimyung 159

hannam 62

cnu 635

inha 717

• 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)

DV = SCI journal articles

　 snu korea pusan donga …

snu 0 2635 3071 3637 …

korea 2635 0 436 1002 …

pusan 3071 436 0 566 …

donga 3637 1002 566 0 …

… … … … … …

• Each number in a cell means the absolute difference (of the number of articles) between two universities

QAP result　 beta P p low p high

Intercept

0 0.958 0.958 0.042

OUT 0.24 0.007 0.007 0.993

IN 0.41 0.04 0.04 0.96

• 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.

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.

Study Discussion II

• At a dyadic level,– Specifically, # of SCI articles is related

to # of in and out links between two universities.