Introduction To Social Network Analysis In Digital Age (11 June2009)

Introduction to Social Network Analysis in Digital Age Dr. Han Woo PARK Visiting Research Fellow Oxford Internet Institute, UK Associate Professor Department of Media & Communication YeungNam University 214-1 Dae-dong, Gyeongsan-si, Gyeongsangbuk-do 712-749 Republic of Korea han [email_address] http:// www.hanpark.net A co-leader of WCU Project : Investigating Internet-based Politics with e-Research Tools. Virtual Knowledge Studio (VKS)

Chap 1 History of SNA

Borgatti et al (2009)

Development of Social Network Analysis Scott (200?), p.3 Scott (1991), p.7

Chap 2 Basic concepts

Chap 3 Network types

Basic types of social networks

Chap 4 Primary indicators

degree: number of direct connections

betweenness: role of broker or gatekeeper

closeness: who has the shortest paths to all others

Valdis (2006)

Chap 5 Distinctive characteristics

Comparison with other methods Scott (1991), p.3

Chap 5 Data collection

Types of SNA data

Whole-network method

Measuring all connections with others in group

Population

Ego-centric method

Snowballing

Sample

A combined method

Hogan (2008)

Bi-linked network of politically active A-list Korean citizen blogs (July 2005) URI=Centre DLP=Left GNP=Right Just A-list blogs exchanging links with politicians

Chap 5 Major techniques

Group, group member, liaison, isolates, dyad, tree Richards (1995)

Bj ö rneborn (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 modeling

Borgatti et al (2009) Structural holes

Borgatti et al (2009) Advantageous position in terms of network topology

Chap 6 Advances in digital age

Figure 2. Dendrogram of clustering results 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 exa mining social behavior online.

http:// participatorysociety.org/wiki/index.php?title = Online_Research

Chap 6 Examples of communication network

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 productive in 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- c ommunication al 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?

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

Web types Year Sum of links (Mean) Density Centraliza tion (%) In Out Web 1.0 Home page 2000 N=245 373 (1.52) 0.006 1.84 69.33 2001 515 (2.10) 0.009 1.19 99.55

Network map of 2000 Blue: GNP: Conservative: Opposition Red: MDP: Liberal: Ruling

Network map of 2001 Star networks without any isolation

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)

Web types Year Sum of links (Mean) Density Centraliza tion (%) In Out Web 1.0 Home page 2000 N=245 373 (1.52) 0.006 1.84 69.33 2001 N=243 267 (1.10) 0.002 1.20 69.67

Network map of 2001 before VS after modification

2000 VS 2001 (after modification) Blue: GNP: Conservative: Opposition Red: MDP: Liberal: Ruling

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

Web types Year Sum of links (Mean) Density Centraliza tion (%) In Out Web 2.0 Blog 2005 N=99 652 (6.59) 0.067 22.07 41.66 2006 589 (5.95) 0.061 20.67 35.10

2005 VS 2006 Blue: GNP: Conservative: Opposition Yellow: Uri: Liberal: Ruling Green: DLP: Progressive: Opposition

Web types Year Sum of links (Mean) Density Centralization (%) Note In Out Web 1.0 (Home page) 2000 N=245 373 (1.52) 0.006 1.84 69.33 Hub but, overall, sparse network 2001 515 (2.10) 0.009 1.19 99.55 Web 2.0 (Blog) 2005 N=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

Types Year Gini Characteristics Web 1.0 (Home page) 2000 N=245 0.984 Sparse knitted Hub-spike network Winner-take-all Navigation-ability Website interface 2001 0.996 Web 2.0 (Blog) 2005 N=99 0.759 Fairly connected Buffer-fly network Participatory Homophily-based Personal-tie interface 2006 0.763

What are advantages of massively-collected hyper-link data using search engines for political and electoral communication research?

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)

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

2 Dec 2007 11 Dec 2007 17 Dec 2007

Network Measures with Three Different Points Network measures 2 Dec 07 11 Dec 2007 17 Dec 2007 Clustering coefficient 2.581 2.368 1.777 Average 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

Chap 6 Examples of knowledge network

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

Mutual information in trilateral Triple Helix relations in Korea

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-Korea Science Citation Index 2002 bio materials organic control medical Co-word network in Korea

Top 49 words with cosine ≥ 0.1 for The Netherlands Science Citation Index 2002 cancer biotech Co-word network in the Netherlands

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 chips display printing

Inter-regional collaboration