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

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

    1. 1. 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)
    2. 2. Chap 1 History of SNA
    3. 3. Borgatti et al (2009)
    4. 4. Development of Social Network Analysis Scott (200?), p.3 Scott (1991), p.7
    5. 5. Chap 2 Basic concepts
    6. 6.
    7. 7.
    8. 8.
    9. 9. Chap 3 Network types
    10. 10. Basic types of social networks
    11. 11. Borgatti et al (2009)
    12. 12. Chap 4 Primary indicators
    13. 13. <ul><li>degree: number of direct connections </li></ul><ul><li>betweenness: role of broker or gatekeeper </li></ul><ul><li>closeness: who has the shortest paths to all others </li></ul>Valdis (2006)
    14. 14. Chap 5 Distinctive characteristics
    15. 15. Comparison with other methods Scott (1991), p.3
    16. 16. Borgatti et al (2009)
    17. 17. Chap 5 Data collection
    18. 18. Types of SNA data <ul><li>Whole-network method </li></ul><ul><li>Measuring all connections with others in group </li></ul><ul><li>Population </li></ul><ul><li>Ego-centric method </li></ul><ul><li>Snowballing </li></ul><ul><li>Sample </li></ul><ul><li>A combined method </li></ul>
    19. 19. Hogan (2008)
    20. 22. 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
    21. 23. Chap 5 Major techniques
    22. 24. Group, group member, liaison, isolates, dyad, tree Richards (1995)
    23. 25. 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
    24. 26. cluster, structural equivalence, block modeling
    25. 27. Borgatti et al (2009) Structural holes
    26. 28. Borgatti et al (2009) Advantageous position in terms of network topology
    27. 29. Chap 6 Advances in digital age
    28. 30. Figure 2. Dendrogram of clustering results   Park (2003)
    29. 31. A comment from those who are NOT doing a link analysis <ul><li>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. </li></ul>
    30. 32. http:// participatorysociety.org/wiki/index.php?title = Online_Research
    31. 33. Chap 6 Examples of communication network
    32. 34. Web indicator for knowledge and information networks <ul><li>Links between sites might not provide for actual knowledge/information flow </li></ul><ul><li>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.) </li></ul><ul><li>Or two universities are more collaborative than .. </li></ul><ul><li>Indicator for quantity, not quality?? </li></ul><ul><li>Hyperlinks tend to reveal both existing and emerging socio- c ommunication al network </li></ul>
    33. 35. Universities in Eurasia (at least 100 hyperlinks)
    34. 36. Universities in Asia (at least 20 hyperlinks)
    35. 37. Universities in Asia (at least 50 hyperlinks)
    36. 38. Summary of ASEM links <ul><li>Clear geographic trends are visible, with most universities connecting mainly to other universities from the same country </li></ul><ul><li>A closed-network among China and Singaporean universities: Collaboration </li></ul><ul><li>Academic digital divide </li></ul><ul><li>European universities (e.g., UK) have more incoming links than Asian ones </li></ul>
    37. 39. <ul><li>How different/similar are hyper-linking practices between Web 1.0 and Web 2.0? </li></ul>
    38. 40. Data collection for Web 1.0 <ul><li>Official homepages of S. Korean MPs </li></ul><ul><li>Manual collection: Observation </li></ul><ul><li>Inter-linkage: Who links to whom matrix </li></ul><ul><li>Explicit links excluding links in board </li></ul><ul><li>2-Year tracking of same MPs: 2000-2001 </li></ul>
    39. 41. 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
    40. 42. Network map of 2000 Blue: GNP: Conservative: Opposition Red: MDP: Liberal: Ruling
    41. 43. Network map of 2001 Star networks without any isolation
    42. 44. Data modification <ul><li>Network metrics and diagrams can be heavily influenced by outliers </li></ul><ul><li>- 김홍신 (Kim) Outdegree: 170 in 2000-2001 </li></ul><ul><li>- 박원홍 (Park) Outdegree: 0 -> 244 (Outlier?) </li></ul><ul><li>한승수 (Han) Outdegree: 0 -> 99 (Outlier?) </li></ul><ul><li>Free to link, and they may not be outlier </li></ul><ul><li>Their sites might have been refurbished to increase SEO(Search Engine Optimization) </li></ul>
    43. 45. 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
    44. 46. Network map of 2001 before VS after modification
    45. 47. 2000 VS 2001 (after modification) Blue: GNP: Conservative: Opposition Red: MDP: Liberal: Ruling
    46. 48. Data collection for Web 2.0 <ul><li>Personal blogs of S. Korean MPs </li></ul><ul><li>Manual collection: Observation </li></ul><ul><li>Blogroll links: Excluding links in postings </li></ul><ul><li>Inter-linkage: Who links to whom matrix </li></ul><ul><li>2-Year tracking of same MPs: 2005-2006 </li></ul><ul><li>Phone interview about usage behaviors </li></ul>
    47. 49. 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
    48. 50. 2005 VS 2006 Blue: GNP: Conservative: Opposition Yellow: Uri: Liberal: Ruling Green: DLP: Progressive: Opposition
    49. 51. 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
    50. 52. 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
    51. 53. <ul><li>What are advantages of massively-collected hyper-link data using search engines for political and electoral communication research? </li></ul>
    52. 54. Difference between public opinion survey and actual turnout in GNP primary <ul><li>Contrary to public opinion survey, Park ran neck-and-neck with Lee </li></ul><ul><ul><li>Lee defeated Park only by 1 .5% point (2,452 votes) </li></ul></ul><ul><ul><li>Furthermore, Park obtained 423 votes more than Lee from delegates, party members, and invited non-partisan participants </li></ul></ul><ul><ul><li>http:// gopkorea.blogs.com/south_korean_politics / </li></ul></ul>
    53. 55. Affiliation network diagram using pages linked to Lee’s and Park’s sites N = 901 (Lee: 215, Park: 692, Shared: 6)
    54. 56. Changes of co-link networks during presidential campaign period <ul><li>Co-(in)link analysis of the 20 websites of the candidates/parties using the Yahoo </li></ul><ul><ul><li>Also web size, incoming links, visitor traffic </li></ul></ul><ul><li>Qualitative complements </li></ul><ul><li>Particularly usefulness : Public opinion surveys could not be published within six days before the 2007 election </li></ul>
    55. 57. 2 Dec 2007 11 Dec 2007 17 Dec 2007
    56. 58. 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
    57. 59. Chap 6 Examples of knowledge network
    58. 60. Knowledge-based innovation <ul><li>There are probably three ways to measure knowledge-based innovation system in terms of networked communication </li></ul><ul><li>Journal articles: Traditional knowledge indicator; Scientometric </li></ul><ul><li>Patent registration: Innovation indicator; Technometric </li></ul><ul><li>Website links: Digital (proxy) indicator; Webometric </li></ul>
    59. 61. Number of papers by Korean authors in the Science Citation Index and bi- and trilateral relations between TH-sectors within the economy
    60. 62. Mutual information in trilateral Triple Helix relations in Korea
    61. 63. 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
    62. 64. 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
    63. 65. Top 49 words with cosine ≥ 0.1 for The Netherlands Science Citation Index 2002 cancer biotech Co-word network in the Netherlands
    64. 66. 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).
    65. 67. 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).
    66. 68. 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
    67. 69. Inter-regional collaboration <ul><li>Network measures </li></ul><ul><li>Centrality: sum of connections </li></ul><ul><li>Density: cohesive properties </li></ul><ul><li>Fragmentation: to identify the key actor whose replacement is extremely urgent if the actor is excluded from the network </li></ul>
    68. 70. Seoul (normalized) centralities and overall network centralization
    69. 71. Density values 1974-2006 for all categories, SCI-only, SSCI-only
    70. 72. Fragmentation value when one key player, Seoul, is removed
    71. 73. Chap 7 Tools & Demonstrations
    72. 74. <ul><li>General/visualization tools: </li></ul><ul><li>UciNet (NetDraw), Pajek , NetMiner </li></ul><ul><li>NodeXL </li></ul><ul><li>Visualization tools: IBM ManyEyes </li></ul><ul><li>Text analysis tools: </li></ul><ul><li>- FullText (KrKwic), ICTA (KINM) </li></ul><ul><li>Webometrics tools for web impact analysis, hyperlink network analysis, etc.: </li></ul><ul><li>LexiURL , SocSciBot , VOSON , IssueCrawler </li></ul>
    73. 75. The end <ul><li>Thank you for listening, and thank you to my assistants </li></ul><ul><li>Han Woo Park , Ph.D. </li></ul><ul><li>Email: [email_address] </li></ul><ul><li>Website: www.hanpark.net </li></ul><ul><li>Partially supported by </li></ul><ul><li>Korea Research Foundation Grant </li></ul>

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