UWS Frontiers class

1. What is a small-world network? Suresh Sood Michael Light (Miller Heiman) Psychology Today, vol. 1, no. 1, May 1967, pp61‐67 ? 1991 Class of Harvard Law

2. GreatMystery14 Suresh S. soody soody www.facebook.com/sureshsood ssood Hero5! twitter.com/soody www.bravenewtalent.com/talent/suresh_sood www.linkedin.com/in/sureshsood GeektoidMangala scuzzy55 suresh.sood@uts.edu.au

3. Giant Global Graph 'll be thinking in the graph. My flights. My friends. Things in my life. My breakfast. What was that? Oh, yogourt, granola, nuts, and fresh fruit, since you ask. Submitted by timbl on Wed, 2007-11-21 http://dig.csail.mit.edu/breadcrumbs/node/215

4. The BBC as Social Graph Topics Events Music Programmes Users Gardening News Food

5. Zuckerberg: “We Are Building A Web Where The Default Is Social”(3rd F8 developer conference, San Francisco) Open (Interest) Graph not only social connections between people, but connections people have with their interests—things, places, brands, and other sites. Yelp might create one around restaurants, Pandora might create one around music. Add some “like” buttons and anytime someone likes a restaurant or song anywhere on the Web with a Facebook like button, that information will flow back into the Open Graph. So that Yelp will know what restaurants you and your friends have liked elsewhere and take that into consideration when giving you recommendations, or Pandora with music, and so on. Facebook is taking some of the information that pops up in people’s realtime streams and baking it into the Web. “The stream is ephemeral,” says Zuckerberg. “It is there for a few hours and then it mostly floats away. Services don’t understand the semantic connections between you and that restaurant.” But now Facebook can. Instead of the Web being defined only by hyperlinks (to the benefit of search engines like Google), Facebook wants it to be defined by social connections, likes and dislikes, interests that are coded and machine-readable. “Our goal is to use the open graph so people can have instantly social experiences wherever they go,” he says. The Open Graph is hugely ambitious. Just wait until Facebook plugs in targeted advertising by: Location, Age, Sex, Keywords, Education, Workplace, Relationship Status, Relationship Interests & Languages. The Open Graph API will allow any page on the Web to have all the features of a Facebook Page. Once implemented, developers can include a number of Facebook Widgets, like the Fan Box, or enable the transformation of any Web page so it functions similar to a Facebook Page.

7. Social Objects Blogging Business Dating Pets Photos Videos Religious Social/Entertainment

8. Canada

9. Canadian Rockies

10. Tiffany Co.

11. LVMH – Louis Vuitton

12. Social Network Representation Primary focus is actors & relationships # actors & attributes Nodes (Actors) connected by Links (Ties/relationship or edge) Links represent flows or transfer material goods or information Adjacency list 1: 2 2: 1, 3 3: 2 1 Graph or sociogram 2 3 Adjacency matrix 1 2 3 0 1 0 1 0 1 0 1 0 1 2 3 Relationship Actors 1 = presence of link 0 = no direct link

13. Key Network Measures Degree Centrality Betweenness Centrality Closeness Centrality Eigenvector Centrality Diana’s Clique krackkite.##h (modified labels) Connector (hub) Vendor Contractor ? Broker Boundary spanners

14. NodeXL - Excel 2007 template for viewing and analyzing network graphs www.codeplex.com/NodeXL

15. UCINET 6 UCINET IV for DOS is free Grab bag of techniques and procedures Matrix centered view rows & columns - actors cell value - relationship Citation Borgatti, S.P., M.G. Everett, and L.C. Freeman. 1999. UCINET 6.0 Version 1.00. Natick: Analytic Technologies. Network analysis requires: ##h file contains meta data about the network ##d file contains the actual data about the network

16. Useful References UCINET user guide Tutorial Prof Hanneman Network Analysis in Marketing (Webster & Morrison 2004) www.insna.org(international network for social analysis)

17. Data Language (DL) Filetype dl nr = 6, nc = 4 row labels embedded col labels embedded data: Dian Norm Coach Sam Mon 0 1 1 0 Tue 1 0 1 1 Wed 1 1 0 0 Thu 0 1 0 0 Fri 1 0 1 1 Sat 1 1 0 0

18. Standard Data Sets BERNARD & KILLWORTH FRATERNITY interactions among students living in a fraternity at a West Virginia college HAM RADIO radio calls made over a one-month period (voice-activated recording device) OFFICE interactions in a small business office. TECHNICAL CAMP 92 COUNTRIES TRADE DATA DAVIS SOUTHERN CLUB WOMEN observed attendance at women’s club in 1930s FREEMAN'S EIES DATA GAGNON & MACRAE PRISON GALASKIEWICZ'S CEO'S AND CLUBS KAPFERER MINE KAPFERER TAILOR SHOP KNOKE BUREAUCRACIES 10 organizations and two relationships – money & info exchange KRACKHARDT HIGH-TECH MANAGERS KRACKHARDT OFFICE CSS NEWCOMB FRATERNITY PADGETT FLORENTINE FAMILIES READ HIGHLAND TRIBES ROETHLISBERGER & DICKSON BANK WIRING ROOM SAMPSON MONASTERY Experimental and case study of social relationships." Doctoral dissertation, Cornell Univ. SCHWIMMER TARO EXCHANGE STOKMAN-ZIEGLER CORPORATE INTERLOCKS THURMAN OFFICE WOLFE PRIMATES ZACHARY KARATE CLUB Borgatti, S.P., Everett, M.G. and Freeman, L.C. 2002. Ucinet 6 for Windows. Harvard: Analytic Technologies.

19. Watts and Strogatz (1998)Collective Dynamics of Small-World Networks, Nature 27 References represent history of the term “network” in systems and complexity science chemistry systems biology artificial neural networks Ecology systems science cellular automata dynamical systems theory Robert Axelrod and Stuart Kauffman Pioneers of Complexity Strogatz - dynamical systems theory (chaos)

20. 20 Modeling a social network Watts – Strogatz (1998) Created a model for small-world networks Local contacts Long-range contacts Effectively incorporated closed triads and short paths into the same model

22. large diameter

25. small diameter

26. almost regularN = 1000 k=10 D = 100 L = 49.51 C = 0.67 N =1000 k= 8-13 D = 14 d = 11.1 C = 0.63 N =1000 k= 5-18 D = 5 L = 4.46 C = 0.01

27. L L C C N rand rand 3.1 3.35 0.11 0.00023 153127 WWW 3.65 2.99 0.79 0.00027 225226 Actors 18.7 12.4 0.080 0.005 4914 Power Grid 2.65 2.25 0.28 0.05 282 C. Elegans Duncan J. Watts & Steven H. Strogatz, Nature393, 440-442 (1998) Real life networks are clustered, large C, but have small average distance L.

28. Duncan J. Watts & Steven H. Strogatz, Nature 393, 440-442 (1998)

30. Duncan Watts - The “New” Science of Networks : Annu. Rev. Sociol. 2004. 30:243–70 Research cluster builds on “…a long tradition of network analysis in sociology and anthropology (Degenne and Forse 1994; Scott 2000; Wasserman and Faust 1994)” “…an even longer history of graph theory in discrete mathematics (Ahuja et al. 1993; Bollobas 1998; West 1996)...” “spurred by the rapidly growing availability of cheap yet powerful computers and large-scale electronic datasets...” scholars come from a variety of disciplines, including “the mathematical, biological, and social sciences...” Watts notes the label “new science of networks” may “strike many sociologists as misleading, given the familiarity (to social network analysts) of many of its central ideas’’ (p. 342). ‘‘Nevertheless,’’ he argues, ‘‘the label does capture the sense of excitement surrounding what is unquestionably a fast developing field----new papers are appearing almost daily----and also the un-precedented degree of synthesis that this excitement has generated across the many disciplines in which network-related problems arise’’ (p. 243).

31. Duncan Watts - The “New” Science of Networks Annu. Rev. Sociol. 2004. 30:243–70 Goal to make “substantial progress on a number of previously intractable problems, reformulating old ideas, introducing new techniques, and uncovering connections between what had seemed to be quite different problems” [Examples of intractable problems - analysis of large-scale, complex networks, the study of the evolution and transformation of complex networks over time, the study of how information, innovations, disease, cultural fads flow/move through complex networks. Old ideas - affiliation networks, the small-world problem and community structure. New techniques - discrete mathematics, the power law, cellular automata and agent-based modelling. Cross-disciplinary connections - similarities in network structure at different levels of scale, from a protein to a human organization to an ecosystem. ]

32. Traditional Practioners & New Networkers Anthropologists Sociologists Social work Psychologists Organizational theorists Physicists Mathematicians/Statisticians Economists Computer Scientists Biologists Ecologists Criminologists Health Professionals

33. Key Scholars (New Science of Networks) Duncan Watts (new theoretic discovery: Small-World effect (Watts and Strogatz, 1998) Former Professor of sociology at Columbia University Principal research scientist at Yahoo! Research External faculty member of SFI Albert-LászlóBarabási (new theoretic discovery Scale-Free feature (Barabási and Albert, 1999) Emil T. Hofman Professor of Physics and Director of the Center for Complex Networks at the University of Notre Dame, USA (http://www.nd.edu/~alb/) Associate of the Center of Cancer Systems Biology at the Dana Farber Cancer Institute, Harvard Mark Newman Professor of Physics and Complex Systems at the University of Michigan External faculty member of the SFI Center for the Study of Complex Systems, University of Michigan ( John Holland /Robert Axelrod) Philip Bonacich Professor of Sociology at UCLA Editor of Journal of Mathematical Sociology Barry Wellman S.D. Clark Professor of Sociology, University of Toronto Head of NetLab International Coordinator, International Network for Social Network Analysis (INSNA) Creator creating INSNA (www.insna.org)

34. Barabási & Albert, Science286, 509 (1999) Scale-Free Model Barabasiand collaborators introduceScale-free networkswith two key rules: (a) growth in time by adding nodes and edges Starting with two connected nodes, add a new node to the network one at a time (b) preferential node attachment New nodes prefer to attach to the more connected nodes

37. April 2010 “Peer Influence Analysis” Two Types Of Mass Influencers

40. Susan Boyle “United Breaks Guitars” Old Spice The Man Your Man Could Smell Like Diaspora http://www.nydailynews.com

41. Marketing in an Unpredictable WorldDuncan Watts & Steve Hasker, Harvard Business Review, September 2006 “The implication for marketing executives is that they should de-emphasize designing, making, and selling would-be hits and focus instead on creating portfolios of products that can be marketed using real-time measurement of and rapid response to consumer feedback”. Target large number of ordinary individuals, and help them share message Target “big seed” of 10,000 people They recruit 5,000 extra people Those people recruit 2,500… Eventually dies out, but get 10,000 extra in process Network thinking replaces instinct The more we can measure and experiment , the more this will be true

42. Watts (2007), A twenty-first century science, Nature, 1 February, Vol 445, p.489 “If handled appropriately, data about Internet-based communication and interactivity could revolutionize our understanding of collective human behaviour”. 14,000 participants were asked to listen to, rate and download songs by unknown bands “Bored at work network” is millions of workers who share media, blog, and IM all day Friend Sense Data (via Friend Sense app on Fb) 1500 respondents,17,500 complete dyads, 80,000 partials and 55,000 individual opinions traditional study cost USD200-300K and 2 years on Fb 2-3K and two months.

43. A Look at the Numbers Worldwide visitors to Twitter approached 10M in Feb 2009, up 700+% vs. Feb 2008 (Comscore) 60+% stopped using Twitter a month after joining (Neilsen Online, via Reuters) Older than you think! 18-24 year olds 12% less likely than average to visit Twitter 25-54 year old crowd is driving this trend 45-54 year olds 36% more likely to visit Twitter, making them the highest indexing age group Next is 25-34 year olds: 30% more likely Twitter rose to over 800,000 users in June 2009, up from 13,000 in 2008** 2007 ~ 5,000 tweets per day 2008, ~ 300,000 tweets per day 2009 ~2.5 million tweets every day End 2009 Tweet growth 1,400% reaching 35 million tweets per day Feb 2010 Twitter sees 50 million tweets created per day or 600 TPS Source :Measuring Tweets, twitter blog, @kevinweil, viewed July 5 2010 <http://blog.twitter.com/2010/02/measuring-tweets.html>. Source: Reuters reporter Alexei Oreskovic. ** comScore study, June 2009 Reported in Marketing Charts, August 17th 2009

44. AWashing Machine !

45. Week 2 stats Total Researchers: 371 (+71 since last week) Total Active Data Collections: 262 (169 Streaming, 59 REST, 34 Curated Collections) (+37) Total finished Data Collections: 36 (+23) Total number of tweets in Database: ~33,234,149 (+15m) Total number of users in Database: ~14,437,195 (+7m) Number of tweets including the word “bieber”: too many Database size: 28.7 GB (x2) Current rate of growth ~ 15 million tweets a week… or about 15 gigs a week.

46. 140kit

48. Social Media Marketing is not Conventional Marketing “a many-to-many mediated communications model in which consumers can interact with the medium, firms can provide content to the medium and, in the most radical departure from traditional marketing environments, consumers can provide commercially oriented content to the medium.” Hoffman & Novak, 1997

50. Digital Breadcrumbs

53. Social Search It could represent a monumental shift in search technology. All major engines analyze the link structure of the Web as a key ingredient in determining what pages are most relevant -- a breakthrough that Google championed when it launched in 1998. A Web page that has a lot of other sites linking to it will rank higher, figuring more prominently in a given search, than one with only a few incoming links. Social search aims to shift power from Web publishers, who create these links, to everyday Internet users by examining their bookmarks or giving them tools to express their opinions. Current technology "delegates to Webmasters to decide what is important for the rest of us," says Bradley Horowitz, director of technology development at Yahoo. "Social search is about democratizing this power.” Yahoo Social Circle by Ben Elgin, January 23, 2006, Business Week

54. Facebook is My Newspaper(Susie Wilkening, http://reachadvisors.typepad.com/ OneRiot.com: Search the realtime web

56. Australia Leads Average Time Spent per Person on Social Media Sites in December 2009

57. Referral & Destination Traffic for April 2010agl.com.au Sites people visit before going to agl.com.au facebook.com (21.19%) google.com (15.57%) google.com.au ( 2.68%) Sites people visit after leaving agl.com.au facebook.com (31.6%) energyaustralia.com.au (< 0.1%) google.com.au (<0.1%)

58. Wikipedia entries well placed on Google Generate articles relating to your organisation,executives and news Monitor articles on wikipedia for reputation management Reference with related entries

59. …Blogs are like conversations with friends. You share what you feel and what excites you about certain things. It's almost as good as being there. The fact that others can Google your topic and read is like tuning into a television station. We all want to know what's out there. Who's doing what, shopping where and what products help others. Blogs are just another way to share all the great things, not so great things and just a part of who we are. An outlet if you will. The blogisphere community is all connect and we make contacts in many ways. Through posts, through twitter conversations, through smaller nit community's, live web casts, and through conferences that we met in person. We make many friends and help each other with lot of topics. Many of us are Mom bloggers who stay at home and have no way of making new friends or communicating with others until we found blogging. Blogging creates friendships and that's what makes us real and connected. 40 year old Mom blogger “nightowlmama” (#260)

60. 55 Linguistic Inquiry & Word Count : “junk words” in content http://www.liwc.net/liwcresearch07.php Cognitive complexity = zexcl + ztentat + znegate + zdiscrepzincl Depression = zI + zphyscal + znegemo – zposemo Liar = – zself – zother – zexcl + znegemo or Honesty = zself + zother + zexcl - znegemo - zmotion Female = zself – zsixltr +z other + znegate – zarticle – zpreps + zcertain + zsocial + zpresent – zspace – zoccup + zhome – zmoney Aging = zposemo – zI + zsixltr + zcogmech + zexcl + zfuture – zpast – ztime Presidential = zsixltr – zwps – zunique – zpronoun – zself – zyou – zother – znegate + zarticle + zprep Slatcher, R.B., Chung, C.K., Pennebaker, J.W., & Stone, L.D. (2007), Winning words: Individual differences in linguistic style among U.S. presidential and vice presidential candidates, Journal of Research in Personality, 41, 63-75.

61. Linguistic Inquiry and Word Count (LIWC)Text Analysis : The Psychological Power of Words 56 Pennebaker, J. W., Francis ME, Booth RJ. (2001). Linguistic Inquiry and Word Count (LIWC): LIWC2001. Mahwah: Lawrence Erlbaum Associates.

62. Brand Equity - Conversational Conversation Gap (Rubel 2005) Brand share of the online conversation Gap between the total number of conversations about a category and the proportion which mention the brand operating in the category Equities of a Brand (Stein 2006) Topics being mentioned in conversations about a brand with equity share corresponding to the frequency at which each topic is mentioned See pp 115-116 Cook, N 2008. Enterprise 2.0 Hampshire,England: Gower Publishing 57

63. Conversation Gap - Vacation and Paris 58 * Total identified blogs: 99,181,005 @ 18 December, 2008

64. Paris – Equity Share Analysis of Attributes 59 * Total identified blogs: 99,181,005 @ 18 December, 2008

65. Blog Mentions Sydney Opera House, TaJMahal & Great Wall China A review of the blogosphere on 8 June 2010 reveals 126.87 million blogs

66. 8 Levels of Social Media Analytics http://manobyte.com/blog/index.php/2008/11/what-is-social-media-analytics orginally adapted from Davenport T (2007), Competing on Analytics

67. YouTube Insight – Video Analytics

68. A New Way of Marketing ? Social Media Marketing 1:1 Marketing ‘All Customers in a network interrelated’ Segment Marketing ‘All Customers are different’ ‘All Customers in a segment the same’ Shotgun Marketing ‘All Customers the same’

69. New (Story Based) Segmentation The most powerful brand voice amongst consumers Crowdsourcing and Culture Mapping Apps = Segmentation Enablers e.g. Foursquare, Farmville Hearts, Keys and Puppetry – Twitter Fairy Tale Neil Gaiman fantasy Writer 124 Contributors over 8 days 10,000 tweets  874 via editorial curation Marie Lenatupot and Tim Stock, What's next for segmentation? Admap Magazine, Feb 2010

70. Levels of User Engagement Curators Moderate a forum Edit a wiki

71. Caution! “Children never put off till tomorrow what will keep them from going to bed tonight” ADVERTISING AGE