The document discusses social networks and social network analysis. It provides definitions of social networks and describes how they can be visualized and analyzed. It also gives examples of popular social networks like MySpace and Facebook, and how people use different social media. Network analysis concepts like degrees of separation, Dunbar's number, and centrality measures are also introduced.

1. Social Network Analysis Prof. Hendrik Speck University of Applied Sciences Kaiserslautern 5 th Karlsruhe Symposium for Knowledge Management in Theory and Praxis October 11, 2007 Karlsruhe, Germany

2. 1. Social Networks 2. Social Network Analysis 3. Social Network Visualization 4. Visualization Examples 5. Conclusion / Discussion Introduction

3. Social Networks

4. Social Networks Definition A social network is a social structure, community, or society made of nodes which are generally accounts, individuals or organizations. It indicates the ways in which they are connected through various social familiarities, affiliations and/or relationships ranging from casual acquaintance to close familial bonds.

5. Visualization Social Networks

6. Profiles and Platforms Social Bookmarking Social Video Sharing Social Photo Sharing Social Encyclopedia Social Community Social Music Community Social Networks

7. Map of Online Commmunities. Spring 2007. Social Networks Source: XKCD. Map of Online Communities. Available: http://imgs.xkcd.com/comics/online_communities.png

8. Upload audio/video you created Publish your own Web pages 0 % Read customer ratings/reviews 50 % Read blogs Participate in discussion boards Use social networking sites Watch peer-generated video Post ratings/reviews Comment on blogs Tag Web pages or other content Use RSS feeds Listen to podcasts Publish or maintain a blog 25 % Source: Li, Charlene. Social Technographics. Forrester Research. 2007. Available: http://www.forrester.com/Research/Document/Excerpt/0,7211,42057,00.html Web 2.0 and Monthly Usage. United States. 2006. Social Networks

9. Use widgets Create my own online video 0 % Use social networking sites 50 % Web 2.0 and Weekly Usage. Age 12-21. United States. 2006. Read a blog Publish your own blog Contribute to a discussion board Use a wiki Listen to podcasts View a video on a video blog site Reviews products or services Read RSS feeds Publish your own web pages Use a tagging services 25 % Source: Li, Charlene. Social Technographics. Forrester Research. 2007. Available: http://www.forrester.com/Research/Document/Excerpt/0,7211,42057,00.html Social Networks

10. 0 % Critics 50 % 52 % 19% Collectors Joiners Spectators Inactives 33 % 19 % 15 % Web 2.0: Consumer Groups by Activity. United States. 2006. Creators 13% Publish Web page or blog / Upload video to videoportals Comment on blogs / Post ratings and reviews Use RSS / Tag Web pages Use social networking sites Read blogs / Watch videos / Listen to podcasts None of these activities Source: Li, Charlene. Social Technographics. Forrester Research. 2007. Available: http://www.forrester.com/Research/Document/Excerpt/0,7211,42057,00.html Social Networks

11. 50,000,000 Evolution of Dance. User. Youtube. 33,000,000 Avril Lavigne. Girlfriend. Youtube. 25,000,000 Pokemon Theme Music Video. User. Youtube. 23,000,000 SNL. Dick in a Box. Youtube. 22,000,000 Desperate Housewives. ABC. USA. TV. 18,000,000 Quick Change Artists. NBC. Youtube. 18,000,000 CSI: Miami. CBS. USA. TV. 17,000,000 Gansta Happy Feet Remix. User. Myspace. 15,500,000 Lost. Second Season Premiere. ABC. USA. TV. Social Networks Audience Comparison. Web 2.0, Social Networks, and Media. June 2007.

12. 15,000,000 Free Hugs Campaign. User. Youtube . 14,000,000 Yomiuri Shimbun. Japan. Newspaper 13,000,000 Beyonce. Irreplaceable. Youtube. 12,000,000 The Asahi Shimbun. Japan. Newspaper. 12,000,000 300 Trailer Video. Warner Bros. Myspace. 10,000,000 Kiwi! Animation. User. Youtube. 8,000,000 Robot Dance. User. Youtube. 6,800,000 Otters holding hands. User. Youtube. 6,000,000 New Numa. User. Youtube. Social Networks Audience Comparison. Web 2.0, Social Networks, and Media. June 2007.

13. 5,600,000 Hips Don't Lie. Shakira. Youtube . 5,600,000 Mainichi Shimbun. Japan. Newspaper. 5,000,000 Fast and the Furious. User. Myspace. 3,900,000 Bild. Germany. Newspaper. 3,100,000 Midget Dancing. User. Myspace. 2,400,000 The Sun. United Kingdom. Newspaper. 2,300,000 USA Today. USA. Newspaper. 2,100,000 The Wall Street Journal. USA. Newspaper. 1,100,000 New York Times. USA. Newspaper Social Networks Audience Comparison. Web 2.0, Social Networks, and Media. June 2007.

14. 1,000,000 Der Spiegel. Germany. Magazine. 820,000 Los Angeles Times. USA. Newspaper. 720,000 New York Post. USA. Newspaper. 700,000 Washington Post. USA. Newspaper. 570,000 Chicago Tribune. USA. Newspaper. 500,000 Houston Chronicle. USA. Newspaper. Social Networks Audience Comparison. Web 2.0, Social Networks, and Media. June 2007.

15. Social Networks Marketshare. February 2007. Rank Name Domain Market Share 1. MySpace www.myspace.com 80.74% 2. Facebook www.facebook.com 10.32% 3. Bebo www.bebo.com 1.18% 4. BlackPlanet.com www.blackplanet.com 0.88% 5. Xanga www.xanga.com 0.87% 6. iMeem www.imeem.com 0.73% 7. Yahoo! 360 360.yahoo.com 0.72% 8. Classmates www.classmates.com 0.72% 9. hi5 www.hi5.com 0.69% 10. Tagged www.tagged.com 0.67% 11. LiveJournal www.livejournal.com 0.49% 12. Gaiaonline.com www.gaiaonline.com 0.48% 13. Friendster www.friendster.com 0.34% 14. Orkut www.orkut.com 0.26% 15. Live Spaces spaces.live.com 0.18% Source: Hitwise. Social Networking Visits Increase 11.5 Percent From January To February . March 2007. Available: http://www.hitwise.com/press-center/hitwiseHS2004/socialnetworkingmarch07.php Social Networks

16. Top 10 Social Networking Sites. April 2006. U.S., Home and Work. Site Apr-05 UA Apr-06 Growth MySpace 8,210 38,359 367% Blogger 10,301 18,508 80% Classmates Online 11,672 12,865 10% YouTube N/A 12,505 N/A MSN Groups 12,352 10,570 -14% AOL Hometown 11,236 9,590 -15% Yahoo! Groups 8,262 9,165 11% MSN Spaces 1,857 7,165 286% Six Apart TypePad 5,065 6,711 32% Xanga.com 5,202 6,631 27% Source: Nielsen/NetRatings. Top 10 Social Networking Sites . April 2006. Available: http://www.nielsen-netratings.com/pr/pr_060511.pdf Social Networks

17. The market share of Internet visits to the top 20 social networking websites grew by 11.5 percent from January to February 2007, to account for 6.5 percent of all Internet visits in February 2007. “ ” Growth Rates and Potential Source: Nielsen/NetRatings. Top 10 Social Networking Sites . April 2006. Available: http://www.nielsen-netratings.com/pr/pr_060511.pdf Social Networks

18. “ The younger generations are consuming information in a different way. …They may not necessarily be going into bookshops. They are spending time on Google, MySpace, Facebook, author Web sites, Yahoo and MSN.” “ ” Brian Murray, Group President of HarperCollins. Source: Rich, Motoko. “HarperCollins Steps Up Its Presence on the Internet.” New York Times. August 3, 2006, Available: http://www.nytimes.com/2006/08/03/books/03book.html Demographic Change Social Networks

19. Social Network Analysis

20. Social Network Analysis Social Network of 09/11 Source: Ron Hutcheson and James Kuhnhenn. “A Wide Surveillance Net.” The Philadelphia Inquirer. May 2006, Available: http://www.orgnet.com/divided2.html

21. History. Social Network Analysis and Sociology. 1700 Sociology Auguste François Xavier Comte 1800 Modern Sociology/Socioeconomics Max Weber/ Ferdinand Tönnies Emile Durkheim/ Karl Marx 1900 Formal Sociology Georg Simmel/ Leopold von Wiese 1950 Sociometry Wolfgang Köhler /Jacob Levy Moreno 1990 Social Network Analysis Albert-Laszlo Barabasi, Howard Rheingold, Yochai Benkler Social Network Analysis

22. Sixth Degree. Dr. Stanley Milgram 1933 – 1984, an American social psycho-logist at Yale University, Harvard University and the City University of New York conducted in 1967 the small-world experiment that is the basis of the six degrees of separation concept. Milgram sent several packages to random people in the United States, asking them to forward the package, by hand, to someone specific or someone who is more likely to know the target. The average path length for the received packages was around 5.5 or six, resulting in widespread acceptance for the term six degrees of separation. Social Networks

23. Sixth Degree. Social Networks

24. 150 - Dunbars Number Dunbar's number, 150, describes the theoretical maximum of a genuine social network. The number is based on the limits of human abilities to identify members, relationships and expres-sions – history, sociology, evolutionary psychology and anthro-pology trace the number back to the the sense of community in various hunter-gatherer societies. Social Networks

25. 150 - Dunbars Number Approximate numbers include the estimated size of a Neolithic farming village (150), the splitting point of Hutterite settlements (150), the upper bound on the number of academics in a discipline's sub-specialization (200), the basic unit size of professional armies in Roman antiquity and in modern times since the 16th century – a company – or Maniple, a tactical unit of the Roman legion, usually includes 75-200 soldiers. Social Networks

26. Types of Data and Analysis Research Method Survey Research Surveys and Interviews Ethographic Research Observations, Field Studies Documentary Research Logfiles, Texts and Archives Social Networks Type of Data Attribute Ideational Relational Type of Analysis Variable Typological Network } Source: Scott, John. Social Network Analysis. Sage Publications. 2000. Paperback, ISBN: 0761963391 }

27. Betweenness Centrality Closeness Centrality Degree Flow Betweenness Centrality Centrality Eigenvector Centralization Clustering Coefficient Cohesion Contagion Attributes and Quantities. Social Network Analysis. Density Integration Path Length Radiality Reach Structural Equivalence Structural Hole Islands Social Networks

28. 1. Account ID 2. User Name 3. First Name 4. Last Name 5. Academic Title 6. Academic Degree 7. Sex/Gender 8. Birth/Maiden Name 9. Relationship Status User Identifiers and Attributes. Social Network Analysis. 10. Sexual Preferences 11. Birthday 12. Sign of the Zodiac 13. Hometown 14. Country 15. Time Zone 16. Political Views 17. Religious Views Social Networks

29. 18. Address 19. City 20. Zip 21. Country 22. Website 23. Email 24. Mobile Phone 25. Land Phone 26. Fax Contact Information. Social Network Analysis. 27. Skype ID 28. ICQ ID 29. AIM ID 30. Yahoo ID 31. WindowsLive ID 32. GoogleTalk ID 33. Gadu-Gadu ID Social Networks

30. 34. Status 35. Employer 36. Position/Title 37. Company Website 38. Address 39. City 40. Zip Code 41. State 42. Country Work. Social Network Analysis. 43. Industry 44. Description 45. Wants 46. Haves 47. Time Period From 48. Time Period To 49. Business Organization Social Networks

31. 50. College/University 51. Class Year 52. Attended for 53. Degree 54. College/Graduate School 55. Concentration 56. Second Concentration 57. Third Concentration 58. Degree Education. Social Network Analysis. 59. High School 60. Class Year Social Networks

32. 61. Activities 62. Interests 63. Hobbies 64. Favorite Music 65. Favorite TV Shows 66. Favorite Movies 67. Favorite Books 68. Favorite Quotes 69. About Me Personal Information and Interests. Social Network Analysis. 70. Pictures 71. Uploaded Picture(s) 72. Picture Tags 73. Audio 74. Uploaded Audio 75. Audio Tags 76. Video 77. Uploaded Video(s) 78. Video Tags Social Networks

33. 79. Location 80. Contacts 81. # of Contacts 82. Messages 83. # of Messages 84. Events 85. # of Events 86. Guestbook Entries 87. # of Guestbook Entries Connection and Usage Information. Social Network Analysis. 88. Online Status 89. Login Time 90. Usage 91. IP Address 92. Network 93. Operating System 94. Browser 95. Screen Size 96. Language Social Networks

34. Social Network Visualization

35. Social Network Analysis. Social Networks

36. Inflow. Social Network Analysis. 2003. Social Networks Source: Valdis Krebs and David Krackhardt. Inflow/ Kite Network. 2003, Available: http://www.orgnet.com/sna.html

37. Degrees/Centrality. Social Network Analyis. Social Networks

38. Betweeness. Social Network Analysis. Social Networks

39. Closeness. Social Network Analysis. Social Networks

40. Male vs. Female. Social Networks

41. Young vs. Old/Slim vs. Overweight/ Weak vs. Strong Social Networks

42. Relationships. Social Networks

43. Weight/ Direction/ Intensity of Relationships Social Networks

44. Visualization Examples

45. Source: Ross Mayfield. Ryze Blog Tribe. Friends Network. December 2002, Available: http://radio.weblogs.com/0114726/2003/01/02.html#a176 Social Network Analysis Ryze Blog. Tribe. 2002. In 2002 Valdis Krebs, Pete Kaminski, and Ross Mayfield mapped the blogspace of Ryze, friendship networks and blogrolls of tribe members. The data of the Blog Tribe, a business network, includes 1,108 nodes of approximately 100 members captured within a month. A webcrawler was used to capture the links between individual members pages and InFlow 3.0 by Valdis was used to map and visualized the social network in its entirety.

46. Source: Ross Mayfield. Ryze Blog Tribe. Friends Network. December 2002, Available: http://radio.weblogs.com/0114726/2003/01/02.html#a176

47. Source: Heer, Jeffrey. Exploring Enron. Visualizing ANLP Results. University of California. 2004, Available: http://jheer.org/enron/v1/ and Keila, P. S. and D. B. Skillicorn. Detecting unusual email communication. IBM Centre for Advanced Studies Conference archive. Proceedings of the 2005 Conference of the Centre for Advanced Studies on Collaborative research. Toranto, Ontario, Canada, Pages 117 – 125, ISSN:1705-7361, Available: http://www.cs.queensu.ca/TechReports/Reports/2005-498.pdf Social Network Analysis Enron, a bankrupt energy company based in Houston employed around 21,000 people and was one of the biggest electricity, natural gas, and communications companies, with claimed revenues of $101 billion in 2000. Fortune magazine named Enron "America's Most Innovative Company" for six consecutive years. In 2001 Enron became the symbol of willful corporate and institutionalized fraud and corruption. Its European operations filed for bankruptcy on November 30, 2001, and it sought Chapter 11 protection in the U.S. two days later, on December 2. Enron. 2004.

48. Social Network Analysis Enron. 2004.

49. Social Network Analysis Enron. 2004.

50. Social Network Analysis Enron. 2004.

51. Social Network Analysis The graph provides a network map of the purchase patterns of political books based on data provided by major web book retailers after the 2006 mid-term elections. Starting with political books included in the New York Times Bestseller List a network of related books is created based on purchase patterns ("Customers who bought this item also bought...“), and visualized according to relationships, network neighborhood, and populace. The resulting graph underlines a divided readership, visualized by two distinct clusters with strong internal affiliations. Extreme positions are located at both sides, the middle ground is only represented by three individual books, eventually a reflection of the electoral debates. Purchase Patterns of Political Books. Orgnet. 2004. Source: Valdis Krebs. Divided We Stand... Still. Available: http://www.orgnet.com/divided2.html

52. Social Network Analysis Purchase Patterns of Political Books. Orgnet. 2004. Source: Valdis Krebs. Divided We Stand... Still. Available: http://www.orgnet.com/divided2.html

53. Source: Goetz, Kristina. “New teen study could help stop spread of sexually transmitted disease.” Columbia News Service. March 15, 2005, Available: http://jscms.jrn.columbia.edu/cns/2005-03-15/goetz-teensex Social Network Analysis Sociologists, mapping the romantic and sexual relationships of an American high school for 18 months revealed that teens, unlike adults, don’t have core groups that are highly sexually active. Instead, teens are linked to many other teens through former sexual connections. The findings can influence social policy and help stop the spread of sexually transmitted diseases among high school students because number of partners a teen has is less important than how he is connected to others in a kind of romantic chain. Jefferson High School. United States. 2005

54. Source: Goetz, Kristina. “New teen study could help stop spread of sexually transmitted disease.” Columbia News Service. March 15, 2005, Available: http://jscms.jrn.columbia.edu/cns/2005-03-15/goetz-teensex Social Network Analysis Using data from the National Longitudinal Study of Adolescent Health, researchers analyzed the interviews and the structures of romantic and sexual relations of more than 800 students at an unidentified high school in the Midwest that they dubbed Jefferson High. Each circle represents a student and lines connecting students represent romantic relations occurring within the 6 months preceding the interviews. Numbers under the figure count the number of times that pattern was observed. Jefferson High School. United States. 2005

55. Social Network Analysis Jefferson High School. United States. 2005 Source: Goetz, Kristina. “New teen study could help stop spread of sexually transmitted disease.” Columbia News Service. March 15, 2005, Available: http://jscms.jrn.columbia.edu/cns/2005-03-15/goetz-teensex

56. Business Network Analysis. 2004. Source: Pascal Jaubert, Frank Langhans, and Prof. Hendrik Speck. Vinex. Business Network Analysis. University of Applied Sciences Kaiserslautern. 2004, Available: http://sourceforge.net/projects/vinex/ Social Network Analysis Vinex (virtual network explorer) reduces the complexity of social networks by visualizing their structures. Based on data collected by a web crawler from virtual communities, vinex offers the possibility to explore relationships between social networks, groups, and individual community members as well as interests, coordinates, profiles, addresses, email addresses, and themes.

61. Studianalyse. 2006/2007. StudiAnalyse, a free software project, aims to analyze and visualize a social community network similar to Facebook. The network, launched in October 2005, is enjoying enormous popularity amongst students in Germany (currently serving more than 1,500,000 users.) Social Network Examples Source: Anna Lewandowski, Jennifer Gliem, Christoph Gerstle, Florian Moritz, and Prof. Hendrik Speck. Studianalyse. University of Applied Sciences Kaiserslautern. 2006/2007, Available: http://www.studianalyse.de.vu/

71. Source: Hagen Fritsch. StudiVZ. Inoffizielle Statistikpräsentation. December 2006, Available: http://studivz.irgendwo.org/ Statistics. StudiVZ. December 2006. Social Network Analysis

72. Source: Hagen Fritsch. StudiVZ. Inoffizielle Statistikpräsentation. December 2006, Available: http://studivz.irgendwo.org/ Statistics. StudiVZ. December 2006. Social Network Analysis

73. Source: Hagen Fritsch. StudiVZ. Inoffizielle Statistikpräsentation. December 2006, Available: http://studivz.irgendwo.org/ Statistics. StudiVZ. December 2006. Social Network Analysis

74. Source: Hagen Fritsch. StudiVZ. Inoffizielle Statistikpräsentation. December 2006, Available: http://studivz.irgendwo.org/ Statistics. StudiVZ. December 2006. Social Network Analysis

75. Source: Hagen Fritsch. StudiVZ. Inoffizielle Statistikpräsentation. December 2006, Available: http://studivz.irgendwo.org/ Social Network Analysis

76. Source: Hagen Fritsch. StudiVZ. Inoffizielle Statistikpräsentation. December 2006, Available: http://studivz.irgendwo.org/ Statistics. StudiVZ. December 2006. Social Network Analysis

77. Source: Hagen Fritsch. StudiVZ. Inoffizielle Statistikpräsentation. December 2006, Available: http://studivz.irgendwo.org/ Statistics. StudiVZ. December 2006. Social Network Analysis

78. Source: Hagen Fritsch. StudiVZ. Inoffizielle Statistikpräsentation. December 2006, Available: http://studivz.irgendwo.org/ Statistics. StudiVZ. December 2006. Social Network Analysis

79. Social Network Analysis Explores and visualizes related search engine results in Google’s database. The results are clustered automatically in categories, additional filters can be used to show, hide or expand individual nodes Nodes are represented by graphical favIcons, URL’s and/or site names; different sizes, shapes, and colors are used to represent categories, relationships, relative importance. The resulting graph provides instant feedback and can be modified and rearranged. Google Browser. Touchgraph. 2007 Source: Touchgraph. Google Browser. Available: http://www.touchgraph.com/TGGoogleBrowser.html

80. Social Network Analysis European Graduate School. Touchgraph. 2007. Source: Touchgraph. Google Browser. Available: http://www.touchgraph.com/TGGoogleBrowser.html

81. Social Network Analysis Massachusetts Institute of Technology. Touchgraph. 2007. Source: Touchgraph. Google Browser. Available: http://www.touchgraph.com/TGGoogleBrowser.html

82. Social Network Analysis Playboy Magazine. Touchgraph. 2007. Source: Touchgraph. Google Browser. Available: http://www.touchgraph.com/TGGoogleBrowser.html

83. Source: Crossway. Co-Occurrences of Names in the New Testament. January 2007, Available: http://services.alphaworks.ibm.com/manyeyes/view/SMGTJEsOtha6GEktsYeKE2- Social Network Analysis Visualizes the occurrence of biblical names in the same chapter of the New Testament. The visualization will connect two people who appear in the text near each other, even though that does not necessarily mean they have a relationship. An ontology, semantic knowledge base or genealogy dataset would improve the visualization. Biblical Figures in New Testament. 2007.

85. Source: W Bradford Paley. Alice in Wonderland TextArc. Information Esthetics. 2002, Available: http://informationesthetics.org/node/25 Social Network Analysis A TextArc visualization of Lewis Carrol’s classic story Alice’s Adventures in Wonderland; the visualization follows a chronological, clockwise pattern; frequently occurring terms are represented by brighter colors, characters mentioned throughout the book are located in the middle, brighter words around the outside suggest that this term or character is episodic in nature. Alice in Wonderland. 2002.

88. Source: On, Josh and Amy Balkin. They Rule. Available: http://www.theyrule.net/ Social Network Analysis Visualizes the relationships between boards and directors of the biggest U.S. companies. Users can browse through interlocking directories, run searches on the boards and companies, save maps of connections complete with annotations and email links to these maps to others. They Rule. 2004.

92. Source: Michael Kabdebo, Marc Rohe, Florian Bielsky, and Prof. Hendrik Speck. Infrastat. Business Network Analyzer. University of Applied Sciences Kaiserslautern. 2007, Available: http://sourceforge.net/projects/infrastat/ Social Network Analysis Infrastat visualizes the relationships between commercial and political interests in Germany, providing a detailed picture of the Deutschland AG. The framework relies on data provided by commercial content providers and visualizes the data within a modular visualization framework. The framework is currently expanded to include political affiliations, campaign contributions, user annotations and references. Infrastat. Economic Network Analyser. 2007.

96. Source: On, Josh and Amy Balkin. Exxon Secrets. 2005, Available: http://www.exxonsecrets.org/ Social Network Analysis Exxon Secrets, developed for Greenpeace, is exposing Exxon-Mobil's funding of climate change skeptics. The application provides a visual interface for the corporate funded anti-environmental movement. Some corporations, think tanks, conservative institutions and their spokespeople, call global warming a hoax. This project pretends to show how ExxonMobil is quietly funding these organizations by exposing the perfidious network of connections between ExxonMobil and the organizations and people that benefit from their funding. Exxon Secrets. 2005.

103. Social Network Analysis SpamCan is a web based software, which attracts spam so the harvester und spammer can be identified und localizied. Spam emails were separated in 10 catagories with help from the Bayesian filters. The programm relies on the Open Source SpamJAM framework in conjunction with new features and components. spamCAN. Spam Analyzer. 2006. Source: Ralf Wagner, Katharina Gerhardt, Yvonne Schmittler, and Prof. Hendrik Speck. SpamCan. Spam Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/spamcanproject/

104. Social Network Analysis spamCAN. Spam Analyzer. 2006. Source: Ralf Wagner, Katharina Gerhardt, Yvonne Schmittler, and Prof. Hendrik Speck. SpamCan. Spam Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/spamcanproject/

105. Social Network Analysis spamCAN. Spam Analyzer. 2006. Source: Ralf Wagner, Katharina Gerhardt, Yvonne Schmittler, and Prof. Hendrik Speck. SpamCan. Spam Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/spamcanproject/

106. Social Network Analysis spamCAN. Spam Analyzer. 2006. Source: Ralf Wagner, Katharina Gerhardt, Yvonne Schmittler, and Prof. Hendrik Speck. SpamCan. Spam Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/spamcanproject/

107. Social Network Analysis spamCAN. Spam Analyzer. 2006. Source: Ralf Wagner, Katharina Gerhardt, Yvonne Schmittler, and Prof. Hendrik Speck. SpamCan. Spam Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/spamcanproject/

108. PeerLo is a plugin for the bittorrent-client "Azureus". The plugin visualizes the bittorrent peers of torrents downloaded. The plugin collects the IP-adresses of the swarm and visualizes them on a map. Peerlow also provides some statistics of the peers location and their downloading/uploading behavior. Social Network Analysis PeerLo. Bittorrent Network Analyzer. 2006. Source: Andreas Augustin, Christian Becker, and Prof. Hendrik Speck. PeerLo. Bittorrent Network Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/peerlo

109. Social Network Analysis PeerLo. Bittorrent Network Analyzer. 2006. Source: Andreas Augustin, Christian Becker, and Prof. Hendrik Speck. PeerLo. Bittorrent Network Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/peerlo

110. Social Network Analysis PeerLo. Bittorrent Network Analyzer. 2006. Source: Andreas Augustin, Christian Becker, and Prof. Hendrik Speck. PeerLo. Bittorrent Network Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/peerlo

111. Social Network Analysis PeerLo. Bittorrent Network Analyzer. 2006. Source: Andreas Augustin, Christian Becker, and Prof. Hendrik Speck. PeerLo. Bittorrent Network Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/peerlo

112. PeerLo. Bittorrent Network Analyzer. 2006. Source: Andreas Augustin, Christian Becker, and Prof. Hendrik Speck. PeerLo. Bittorrent Network Analyzer. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/peerlo Social Network Analysis

113. B.A.S.S (band analysis and suggestion system) is a software application which analyzes, visualizes and calculates music usage based on the social network of www.MySpace.com., or to be more correct, on the social network of musicians within MySpace. B.A.S.S. MySpace Music Analysis. 2006. Social Network Analysis Source: Thomas Klein, Patrick Mai, Fabian Winkhardt, and Prof. Hendrik Speck. BASS. MySpace Music Analysis. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/bass

114. Social Network Analysis B.A.S.S. MySpace Music Analysis. 2006. Source: Thomas Klein, Patrick Mai, Fabian Winkhardt, and Prof. Hendrik Speck. BASS. MySpace Music Analysis. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/bass

115. Social Network Analysis B.A.S.S. MySpace Music Analysis. 2006. Source: Thomas Klein, Patrick Mai, Fabian Winkhardt, and Prof. Hendrik Speck. BASS. MySpace Music Analysis. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/bass

116. Social Network Analysis B.A.S.S. MySpace Music Analysis. 2006. Source: Thomas Klein, Patrick Mai, Fabian Winkhardt, and Prof. Hendrik Speck. BASS. MySpace Music Analysis. University of Applied Sciences Kaiserslautern. 2006, Available: http://sourceforge.net/projects/bass

117. Visualizes the relationships between scientific areas, publications and connecting scientific paradigms of roughly 800,000 published papers categorized by 776 different scientific paradigms. Paradigms are represented by pale circular nodes based on the number of citations, links describe the relationship between paradigms and papers, node proximity and link intensity serve as indicators for the similarity between paradigms. General scientific areas are labeled, flowing labels include word lists unique to individual paradigms. Relationships Among Scientific Paradigms. 2007. Social Network Analysis Source: Boyack, Kevin , Dick Klavans, and W. Bradford Paley. "Relationships Among Scientific Paradigms." Seed. March 7, 2007, Available: http://seedmagazine.com/news/2007/03/scientific_method_relationship.php

118. Relationships Among Scientific Paradigms. 2007. Social Network Analysis Source: Boyack, Kevin , Dick Klavans, and W. Bradford Paley. "Relationships Among Scientific Paradigms." Seed. March 7, 2007, Available: http://seedmagazine.com/news/2007/03/scientific_method_relationship.php

119. Relationships Among Scientific Paradigms. 2007. Social Network Analysis Source: Boyack, Kevin , Dick Klavans, and W. Bradford Paley. "Relationships Among Scientific Paradigms." Seed. March 7, 2007, Available: http://seedmagazine.com/news/2007/03/scientific_method_relationship.php

120. Relationships Among Scientific Paradigms. 2007. Social Network Analysis Source: Boyack, Kevin , Dick Klavans, and W. Bradford Paley. "Relationships Among Scientific Paradigms." Seed. March 7, 2007, Available: http://seedmagazine.com/news/2007/03/scientific_method_relationship.php

121. Die Lokalisten. Social Network. 2007. Social Network Analysis Source: Heinen, Felix. Datenvisualisierung eines sozialen Netzwerks. Die Lokalisten. University of Applied Sciences Nürnberg. Diploma Thesis. 2007, Available: http://www.felixheinen.de/020.html Visualizes the interpersonal connections within Die Lokalisten, a German social network focusing on the “Who” aspect, especially profile information, network usage, membership status, age, educational background, family status, gender and other demographic data. A second visualization focuses on the “Where” – including but not limited to geographic location, membership distribution, local clusters and exchange processes

122. Die Lokalisten. Social Network. 2007. Social Network Analysis Source: Heinen, Felix. Datenvisualisierung eines sozialen Netzwerks. Die Lokalisten. University of Applied Sciences Nürnberg. Diploma Thesis. 2007, Available: http://www.felixheinen.de/020.html

123. Die Lokalisten. Social Network. 2007. Social Network Analysis Source: Heinen, Felix. Datenvisualisierung eines sozialen Netzwerks. Die Lokalisten. University of Applied Sciences Nürnberg. Diploma Thesis. 2007, Available: http://www.felixheinen.de/020.html

124. Discussion/Conclusion

125. Thank you for your attention. I will gladly answer your questions. Prof. Hendrik Speck contact (at) hendrikspeck [dot] com University of Applied Sciences Kaiserslautern Information Architecture Lab Amerikastrasse 1 66482 Zweibrücken Tel: +49 6332 914 360 Skype: hendrikspeck Conclusion Contact Information

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