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Introduction to Social Network Analysis in Digital
Age
Dr. Han Woo PARKVisiting Research Fellow Oxford Internet Institute, UK
Associate ProfessorDepartment of Media & CommunicationYeungNam University214-1 Dae-dong, Gyeongsan-si, Gyeongsangbuk-do 712-749Republic of [email protected]://www.hanpark.net
A co-leader of WCU Project: Investigating Internet-based Politics with e-Research Tools.
Virtual Knowledge Studio (VKS)
1
Chap 1History of SNA
2
3
Borgatti et al (2009)
Development of Social Network Development of Social Network AnalysisAnalysis
Scott (200?), p.3
Scott (1991), p.7
Chap 2 Basic concepts
5
6
7
8
Chap 3Network types
9
Basic types of social networks
11Borgatti et al (2009)
Chap 4Primary indicators
12
• degree: number of direct connections
• betweenness: role of broker or gatekeeper
• closeness: who has the shortest paths to all others
Valdis (2006)
Chap 5Distinctive
characteristics
14
Comparison with other Comparison with other methodsmethods
Scott (1991), p.3
16Borgatti et al (2009)
Chap 5Data collection
17
Types of SNA data• Whole-network method- Measuring all connections with others
in group - Population
• Ego-centric method- Snowballing- Sample
• A combined method
19Hogan (2008)
Bi-linked network of politically active A-list Korean citizen blogs (July 2005)
URI=CentreDLP=LeftGNP=Right
Just A-list blogs exchanging links with politicians
Chap 5Major techniques
23
Group, group member, liaison, isolates, dyad, treeGroup, group member, liaison, isolates, dyad, tree
Richards (1995)
Björneborn (2003)
* Co-inlink: a link to two different nodes from a third node * Co-outlink: A link from two different nodes to a third node
cluster, structural equivalence, block modelingcluster, structural equivalence, block modeling
27
Borgatti et al (2009)
Structural holesStructural holes
28Borgatti et al (2009)
Advantageous position in terms of network topologyAdvantageous position in terms of network topology
Chap 6Advances in digital age
29
Park (2003)
A comment from those who are NOT doing a link analysis
• In a chapter of The Sage Handbook of Online Research Methods edited by Fielding et al. (2008), Horgan emphasizes that ‘link analysis’ has become an active research domain in examining social behavior online.
31
http://participatorysociety.org/wiki/index.php?title=Online_Research
Chap 6Examples of
communication network
33
Web indicator for knowledge and information
networks• Links between sites might not provide for actual
knowledge/information flow
• But one university receives more links from another, this can be because it is more productivein terms of scholarly performance (e.g., journal article publications, class materials, pre-prints etc.)
• Or two universities are more collaborative than ..
• Indicator for quantity, not quality??
• Hyperlinks tend to reveal both existing and emerging socio-communicational network
Universities in Eurasia (at least 100 hyperlinks)
Universities in Asia (at least 20 hyperlinks)
Universities in Asia (at least 50 hyperlinks)
Summary of ASEM links
• Clear geographic trends are visible, with most universities connecting mainly to other universities from the same country
• A closed-network among China and Singaporean universities: Collaboration
• Academic digital divide- European universities (e.g., UK) have
more incoming links than Asian ones
• How different/similar are hyper-linking practices between Web 1.0 and Web 2.0?
39
Data collection for Web 1.0
• Official homepages of S. Korean MPs• Manual collection: Observation• Inter-linkage: Who links to whom
matrix• Explicit links excluding links in board• 2-Year tracking of same MPs: 2000-
2001
40
Web type
s
Year Sum of
links(Mea
n)
Density
Centralization(%)
In Out
Web 1.0
Home
page
2000N=24
5
373(1.52
)
0.006 1.84 69.33
2001 515(2.10
)
0.009 1.19 99.55
41
Network map of 2000
Blue: GNP: Conservative: Opposition
Red: MDP: Liberal: Ruling 42
Network map of 2001
Star networks without any isolation43
Data modification
• Network metrics and diagrams can be heavily influenced by outliers
- 김홍신 (Kim) Outdegree: 170 in 2000-2001- 박원홍 (Park) Outdegree: 0 -> 244
(Outlier?)- 한승수 (Han) Outdegree: 0 -> 99 (Outlier?)
• Free to link, and they may not be outlier• Their sites might have been refurbished to
increase SEO(Search Engine Optimization)
44
Web type
s
Year Sum of
links(Mea
n)
Density
Centralization(%)
In Out
Web 1.0
Home
page
2000N=24
5
373(1.52
)
0.006 1.84 69.33
2001N=24
3
267(1.10
)
0.002 1.20 69.67
45
Network map of 2001before VS after
modification
46
2000 VS 2001 (after modification)
Blue: GNP: Conservative: Opposition
Red: MDP: Liberal: Ruling 47
Data collection for Web 2.0
• Personal blogs of S. Korean MPs• Manual collection: Observation• Blogroll links: Excluding links in postings• Inter-linkage: Who links to whom matrix• 2-Year tracking of same MPs: 2005-2006• Phone interview about usage behaviors
48
Web type
s
Year Sum of
links(Mea
n)
Density
Centralization(%)
In Out
Web 2.0
Blog
2005N=9
9
652(6.59
)
0.067 22.07
41.66
2006 589(5.95
)
0.061 20.67
35.10
49
2005 VS 2006
Blue: GNP: Conservative: Opposition
Yellow: Uri: Liberal: RulingGreen: DLP: Progressive:
Opposition50
Web types
Year Sum of
links(Mea
n)
Density
Centralization
(%)
Note
In Out
Web 1.0
(Homepage)
2000N=245
373(1.52)
0.006 1.84 69.33 Hub but,
overall,
sparse networ
k
2001 515(2.10)
0.009 1.19 99.55
Web 2.0
(Blog)
2005N=99
652(6.59)
0.067 22.07 41.66 Disappearing
hub but
getting denser
2006 589(5.95)
0.061 20.67 35.10 51
Types
Year Gini Characteristics
Web 1.0
(Home
page)
2000N=245
0.984 Sparse knittedHub-spike network
Winner-take-allNavigation-abilityWebsite interface
2001 0.996
Web 2.0
(Blog)
2005N=99
0.759 Fairly connectedBuffer-fly network
ParticipatoryHomophily-based
Personal-tie interface
2006 0.763
52
• What are advantages of massively-collected hyper-link data using search engines for political and electoral communication research?
53
Difference between public opinion survey and actual turnout in GNP
primary • Contrary to public
opinion survey, Park ran neck-and-neck with Lee– Lee defeated Park only by
1.5% point (2,452 votes)– Furthermore, Park
obtained 423 votes more than Lee from delegates, party members, and invited non-partisan participants
http://gopkorea.blogs.com/south_korean_politics/
Affiliation network diagram using pages linked to Lee’s and Park’s sites
N = 901 (Lee: 215, Park: 692, Shared: 6)
56
Changes of co-link networks during presidential campaign
period • Co-(in)link analysis of the 20 websites
of the candidates/parties using the Yahoo – Also web size, incoming links, visitor traffic
• Qualitative complements• Particularly usefulness: Public opinion
surveys could not be published within six days before the 2007 election
57
2 Dec 2007
11 Dec 2007
17 Dec 2007
58
Network measures 2 Dec 07 11 Dec 2007 17 Dec 2007Clustering coefficient 2.581 2.368 1.777Average distance
(Cohesion value)
1.564
(0.215)
1.821
(0.273)
1.681
(0.346)Degree centralities
of sites
ijworld.or.kr
leehc.org
ckp.kr
0.158
0.000
0.000
0.263
0.053
0.053
0.684
0.263
0.053
Network Measures with Three Different Points
Chap 6Examples of
knowledge network
59
Knowledge-based innovation
• There are probably three ways to measure knowledge-based innovation system in terms of networked communication
- Journal articles: Traditional knowledge indicator; Scientometric
- Patent registration: Innovation indicator; Technometric
- Website links: Digital (proxy) indicator; Webometric
Number of papers by Korean authors in the Science Citation Index and bi- and trilateral
relations between TH-sectors within the economy
R2 > 0.99
0
5,000
10,000
15,000
20,000
25,000
30,000
1970 1975 1980 1985 1990 1995 2000 2005 2010
Nr
of
Ko
rea
n p
ap
ers
in
th
e SCI
Total
University
Industry
Government
UI
UG
IG
UIG
Mutual information in trilateral Triple Helix relations in Korea
-140
-120
-100
-80
-60
-40
-20
0
20
1970 1975 1980 1985 1990 1995 2000 2005 2010
T(u
ig)
in m
bit
s o
f in
form
ati
on T(uig)
2-year moving average
Source: Science Citation Index 2000
2002-70.7
-71.0-45.3
-54.0-39.6-42.5-32.5-27.6-32.8
-82.4-11.0-18.0-28.6-33.7
-18.9-67.7-26.8
Top 68 title words with cosine ≥ 0.1 for South-KoreaScience Citation Index 2002
bio
materials
organic
control medical
Co-word network in Korea
Top 49 words with cosine ≥ 0.1 for The NetherlandsScience Citation Index 2002
cancer
biotech
Co-word network in the Netherlands
chemistry
flowers medical systems
electro-technical
cars
coating
energy
Cosine normalized map of 105 co-occurring words in patents (in 2002) with a Dutch address among the assignees or inventors (N Patents = 2,824; Word frequency > 22; cosine ≥ 0.1).
Cosine normalized map of 103 co-occurring words in patents (2002) with a Korean address among the assignees or inventors (N Patents is 4,200; Word frequency > 40; cosine ≥ 0.1).
Cosine normalized map of 103 co-occurring words in patents (2002) with a Korean address among the assignees or inventors (N Patents is 4,200; Word frequency > 40; cosine ≥ 0.1).
info devices
coating
chipsdisplay
printing
Inter-regional collaboration
• Network measures- Centrality: sum of connections- Density: cohesive properties- Fragmentation: to identify the key
actor whose replacement is extremely urgent if the actor is excluded from the network
Seoul (normalized) centralities and overall network centralization
Density values 1974-2006 for all categories, SCI-only, SSCI-only
Fragmentation value when one key player, Seoul, is
removed
Chap 7Tools &
Demonstrations
73
General/visualization tools: - UciNet(NetDraw), Pajek, NetMiner NodeXL
Visualization tools: IBM ManyEyes
Text analysis tools: - FullText(KrKwic), ICTA(KINM)
Webometrics tools for web impact analysis, hyperlink network analysis, etc.:
- LexiURL, SocSciBot, VOSON, IssueCrawler
The end
Thank you for listening, and thank you to my assistants
Han Woo Park, Ph.D.Email: [email protected]: www.hanpark.net
Partially supported by Korea Research Foundation Grant
75