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 Koreahanpark@ynu.ac.krhttp://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

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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)

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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

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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

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• 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)

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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

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2 Dec 2007

11 Dec 2007

17 Dec 2007

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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

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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: hanpark@ynu.ac.krWebsite: www.hanpark.net

Partially supported by Korea Research Foundation Grant

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