Using Social Media to Leverage Triple Helix Insights in Innovation Ecosystems

1. http://www.flickr.com/photos/angelinux/3034564360/
Using Social Media to Leverage
Triple Helix Insights in
Innovation Ecosystems
Innovation Ecosystems Network
Stanford Media X Led Research Initiative

2. The Innovation Ecosystems Network (IEN) brings together an
international interdisciplinary team that seeks to develop and
diffuse novel data and tools for understanding the catalytic impact
of regional ICT experiments.
http://www.innovation-ecosystems.org

3. Structure of the presentation
• Background of Analysis of Innovation
• Proposed Framework
– Data-driven
– Data Mining and Analysis
– Visualization
• Cases
– Regional
– Sectoral
– Gender
– University
• Discussion

4. Innovation takes at least two.
Team skills are required.
There are winners and loosers.
Although people can communicate anywhere,
anytime, it’s difficult for anyone to have all the
insights necessary at any one time for major
decisions on the complex global issues

5. Events
Impacts
Coalitions
& Networks
Shared
Vision
Transformation
Measure, Analyze & Visualize
DATA  ANALYSIS  MEANING
Interaction & Feedback
Value Co-Creation
IEN Transformation Framework
Translate, measure and transform an innovation ecosystem

6. Research Problem/Questions
Theme: How can data-driven visual social network analysis
provide insights to catalyze innovation ecosystems?
We look for insights that can be used to:
• Identify and empower influential activate the evolution of the ecosystem
• Develop and implement programs (meetings, funding, initiatives) to foster
co-creator networks
• Communicate complexity to co-create vision
• Measure the transformation of an innovation ecosystem
How do co-creation networks enable local/regional ROI on
innovation investments made for globalization?
Work in progress!
Collaboration is invited!
www.innovation-ecosystems.org

7. Models of innovation
Economic Innovation
(producer innovation)-
Schumpeter 1934
End-user Innovation-
von Hippel 1986
Strategic
Innovation- Hamel
and Prahalad 1994
Open innovation-
Chesbrough 2003
Collaborative
Innovation
Networks- Gloor
2005

8. The Way We USED to Think About Organizations New Organizational Chart Based on Relationships
Relationship-Focused Co-Creation Infrastructure
(Visual) Social Network Analysis
“. . . allows investigators to gain new insights
into the patterning of social connections, and it
helps investigators to communicate their results
to others.“ (Freeman, 2009)
Infrastructure for Resource Flows
- - - Relationships
(Companies are interlocked
through key people –
information flow, norms,
mental models.(Davis,1996)

9. Relationship Interlocks
• Executives and key employees
– Transfer of technologies and knowledge, professional networks, business
culture, value-chain resources
• Directors
– US Fortune 500 firms interlocked (shared directors) with average 7 other firms
• Corporate governance embedded and filtered through social structures
– Executive compensation, strategies for takeovers, defending against takeovers
• Gerald F. Davis, “The Significance of Board Interlocks for Corporate Governance,” Corporate Governance 4:3, 1996
• Investors and service providers
– Awareness of external forces, competitive insights, resource leverage
• Relationship interlocks provide
– Social relationship “filter” for governance, information flow & norms
– Transfer of implicit and explicit know-how
– Mental models

10. Through personal relationships
companies have interconnects

11. Social Roles in Social Media
Journal of Social Structure
“Visualizing the Signatures of Social Roles in Online Discussion Groups”
http://www.cmu.edu/joss/content/articles/volume8/Welser/
• Answer person
– Outward ties to local isolates
– Relative absence of triangles
– Few intense ties
• Reply Magnet
– Ties from local isolates often inward only
– Sparse, few triangles
– Few intense ties
 Discussion person
 Ties from local isolates often inward only
 Dense, many triangles
 Numerous intense ties

12. Imagesource:InternetPioneers:PaulBaranatibiblio.org
Network Structures

13. Archetypes of Social Business
Design
• Ecosystem – a community of
connections
• HiveMind – a socially
calibrated mindset of
individuals
• Dynamic Signal - the constant
multi-faceted means of
collaboration
• Metafilter- a method of
finding signals in vast amounts
of noise

14. http://www.fabcats.org/owners/feeding/info.html http://www.flickr.com/photos/manpsing/2618332693/
Passive Learning Active Learning

15. http://www.flickr.com/photos/tomatoskin/1339929731/
Traditional Data Gathering Methods

16. http://www.flickr.com/photos/clydeorama/3495284608/
Have to react QUICKLY

17. Data to measure/track innovation?

18. IEN Dataset
Martha
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.
Updated quarterly with rapid growth each quarter

19. Making Relationships Visible
Wille Framework, OPAALS, 2010.

20. New Data & New Tools
Accessing Data Streams about Innovation Building a Dataset on Innovation
Crystallisation Through Visualisation The Card-Mackinlay-Shneiderman visualisation
reference model:(Card et al., 1999; Miksch, 2005)

21. Trends & Innovation
Patterns & Outliers

22. http://www.flickr.com/photos/ritavitafinzi/2192500407/

23. Tan,Steinbach,Kumar;2004
“There is no data like more data”
(Mercer at Arden. House, 1985)
2,000 points 500 Points8,000 points
“There is no data like more data”
(Mercer at Arden. House, 1985)

24. http://www.iro.umontreal.ca/~bengioy/yoshua_en/research_files/CurseDimensionality.jpg
Could be easier to find patterns
More Data /
More Dimensions
http://wissrech.ins.uni-
bonn.de/research/projects/engel/engelpr2/pr2_thumb.jp
g

25. Easy, fast integration, using APIs and data sources to
produce results that were not the original reason for
producing the raw source data [wiki].
25
Mashup

26. http://www.stat.columbia.edu/~cook/movabletype/archives/2006/04/a_wallful_of_da.html
Information Overload

27. Place of innovation
Localized concentrations -
Marshall 1890
Menlo Park (Research Park) 1948, Stanford Industrial Park 1951,
Research Triangle Park 1959
Clusters- Porter
1998, Saxenian
1994
Regional
Innovation
Systems-
Metcalfe 1995
Innovation
Cluster- Yim
2008, 2002

28. .
Technology-based companies - worldwide, Dec 2009
35,000 companies include:
Sectors: Advertising, biotech, cleantech, consulting, ecommerce, enterprise,
games_video, hardware, legal, mobile, network_hosting, public relations, search,
security, semiconductor, software, web, other firms serving these.
Investment profiles from Ltd to public, financing rounds identified
Merger & Acquisition profiles
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

29. .
.
Employees in technology-based companies - worldwide, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

30. .
Technology-based companies – worldwide
by employee size and sector, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

31. Number of European Technology-based companies
Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

32. .
Number of European Technology-based companies
by employee size, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

33. Number of European Technology-based companies
By sector, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

34. European Technology-based companies
By sector and number of employees, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

35. # of Companies # of People
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

36. .
Number of US Technology-based companies
By sector, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

37. .
Number of US Technology-based companies
Advertising & Web, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.”
Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

38. .
Number of US Technology-based companies
Biotech & Cleantech, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.”
Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”

39. .
Number of Technology-based companies
In Silicon Valley by sector, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.”
Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”

40. .
Number of Technology-based companies
In Seattle by sector, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.”
Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”

41. .
Number of Technology-based companies
In DC by sector, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.”
Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”

42. .
Number of Technology-based companies
In New York City by sector, Dec 2009
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.”
Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”

43. NYCSilicon Valley
BostonSeattle
NeilRubens,KaisaStill,JukkaHuhtamaki,MarthaG.Russell“BehindtheInnovationCurtain:MobilePlayersandTheir
Moves.”
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
Technical Report. Media X, Stanford University, Feb.2010.

44. /Users/neil/Dropbox/2010.06.InJo.IEN/timeline
Dynamics

45. Distance
Old
New

46. The new maps may be
based on the connections
- rather than on distance.

47. Networks of Female and Male Executives
in Companies in the Clean Tech Sector
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

48. Networks of Female and Male Executives
in Companies in the Biotech Sector
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

49. Networks of Female and Male Executives
in Companies in the Public Relations Sector
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

50. Networks of Female and Male Executives
in Companies in the Web Services Sector
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

52. Context of Investments into/from China
Insights into
Innovation
Social
Network
Analysis
Socially
Constructed
Data
Socially constructed dataset, in English, openly
available– all challenges in China
Innovation Ecosystems Dataset:
•323 technology-based companies with one or
more locations in China
•42 Chinese, 77 foreign investment firm
•Investment into China US$ 5.4 B
•Investment originating from China
US$ 3.1 B
Insights explored:
The flow of financial resources into and out of
China
More illustrative than descriptive/prescriptive
results
Innovation Ecosystem Network
Example: Chinese International Investments
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell, A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal
of Networks, Fall, 2010.

53. Initial Data Analysis:
53% (113) of the Chinese companies
from eCIS business sector
50 % (66) of the foreign companies
are from the eCIS business sector
Toward Insights about:
Patterns and differences in the
characteristics of investment flows
into and from China
More Specific: Context of eCIS sector
eCommerce and electronic security=
eCommerce, software search, network hosting, mobile, games &video, enterprise
Insights into
Innovation
Social
Network
Analysis
Socially
Constructed
Data
Innovation Ecosystem Network
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell, A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal
of Networks, Fall, 2010.

54. Network Metrics

55. HARVEST
Investments from Chinese
(making investments)
Innovation Ecosystem Network
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell, A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal
of Networks, Fall, 2010.

56. CULTIVATION
Investments into China
(receiving investments)
Innovation Ecosystem Network
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell
A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal of Networks, Fall, 2010.

57. Emerging Chinese business clusters linked
by firms’ relationships
Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell
A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal of Networks, Fall, 2010.

58. • Cultivation / Harvesting modes - value co-
creation
• Chinese interlocks at the investment firm level
– Government-led investment firms
– Knowledge of government guarantees
– Investments in firms that return benefits to China
• Global interlocks at both investment firm and
enterprise levels
• Opportunity network & value co-creation
http://successbeginstoday.org/wordpress/wp-content/unexpected2.jpg
Topline Findings

59. http://www.flickr.com/photos/arena_provietnam/3544601667/sizes/m/in/photostream/

60. Executive Women
in Technology-Based Companies
• We asked:
– What interventions can attract more women to leadership
positions in technology-based companies?
• Important because
– National dependence on science, technology, math for
competitiveness
– Shortage of women entering, staying, leading
– In tech-based companies,
• roughly 8 % executives are women
• Companies in our sample showed:
– Corroborated recent EU reports
– Cleantech sector lowest
• Fewer women in: enterprise, semiconductor and mobile
– Biotech sector highest
• More women in public relations, legal, consulting

61. Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
Networks of Female Executives
in Companies – All Sectors

62. Networks of Female and Male Executives
in Companies in the Web Services Sector
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

63. CTOs
the CTO of doctr.com CTO at Hemarina
Co-founder of HEMARINA
Vice President Engineering
at Survey Monkey
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

64. R&D Execs
Principal Research
Scientist at Yahoo!
Chief Software Editor at
Yandex
Advisor at PlaceBlogger
Was a VP at Netscape
and AOL, a senior
director of Product
Development at Yahoo
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

65. Creative CMOs – Biz Dev
Product Marketing
Manager at Google
Worked in Public
Relations at Apple,
Marketing at Tiny
Pictures,
VP, Marketing at Loopt
Was VP Marketing at
Project Playlist
Director of User
Experienceof Kosmix
Was Principal UI Designer
at Bebo
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

66. CEOs
CEO of NUS Enterprise at the
National University of
Singapore
Was Managing Director,
Investments, of Bio*One
Capital Pte Ltd
CEO of SeedCamp
was part of the Venture
team at 3i
CEO of Piazzza
worked at Facebook on
their News Feed Team
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

67. Board Members
Co-Chair Disney Media
Networks and President,
Disney-ABC Television
Group
CEO of FON USA and president
of the US Advisory Board
Board of Directors of Posit
Science, Sabrix, AccountNow,
Danger, QuinStreet, The
Threshold Group and the
Board of Directors of the NVCA
Advisor at Betwyx
Was VP, Western
Sales at Glam Media.
Was VP of sales at
MySapce
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

68. Founders
Founder/CEO
SmartWork Network
Co-founded Flickr
Ran Yhoo Tech Dev group
Co-founded Brickhouse
Now product Officer at
Hunch
Founder of
TinyMassive
Formerly COO
Wireless @
Realhome.com
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

69. FoundersFounder of Google
Webmaster Central
Now works for Ignition
Partners as an ‘entrepreneur
in residence’
CEO and Founder of LiveHit
Was Vice President of
Products and Marketing at
Piczo
Co-founder at
23andMe
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

70. Investors
Investor at
InnerRewards
Was executive
digital strategist
at Johnson &
Johnson
Invested in Fluidinfo; The
Extrodinaries; Factual;
Vizu;Square; Vurve;
Fluidinfo; ChallengePost;
Airship Ventures; Joobili;
Dopplr; Wee Web
A Partner with Accel Partners
Board at Wetpaint; Trulia;
Kosmix; Jaser Design; Imperva;
Forescout; Glam Media
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report

71. Figure 1: Point-to-Point Figure 2: Tree Figure 3: Complex Tree
Figure 4: Simple Star Figure 5: Complex Star Figure 6: Mesh Figure 7: Complex Mesh
Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
Type of Networks in Technology-Based Companies
Company Executives, Investors and Board Members

72. NETWORKS MATTER

73. http://fe-male.appspot.com/
In collaboration with:
Under active development, algorithm may change without notice

74. http://www.slowtrav.com/blog/chiocciola/Geirangerfjord.jpg

75. The Norwegian Puzzle
• Norway is a wealthy country with high standard
of living and almost NO unemployment
• Yet, low rate of technology-based innovation
• What is the future of Norway after oil reserves
have been extracted?
• Given technology targets:
– How can innovation be catalyzed?
– How can establishing global relationships be
accelerated?

77. Example: Norwegian Tech-based Companies
Their Branch Offices and Their Financial Orgs
Links show relationships
PRELIMINARY
Example view to IEN dataset in Gephi. Companies are selected with
keyword search “Norway + Norwegian;” the funding organizations
associated with those companies are added Nodes represent
companies and their investors; edges indicate resource flows. The
network layout is created with Yifan Hu Multilevel algorithm and nodes
are inflated according to their indegree, i.e. the number of the
connected investors.

78. Advisors & Investors Expand Access
Investors leverage co-creation opportunities
with investments in multiple companies. Intl
companies not shown.
Companies leverage value co-creation
opportunities through relationships with
multiple investors. Some investors are
international.
Timeline analysis of investment events
reveals patterns of co-investment – an
indication of intention to co-create value
and, perhaps, stimulus programs.
IEN Dataset, July 2010

79. International Relationships for
Value Co-Creation
Huge opportunities for
international
relationships lie 2 & 3
degrees out from
Norwegian companies
Example view to IEN dataset for keyword search. Nodes represent companies and their previous and current employees. The
network layout is created with Fruchterman Reingold algorithm and nodes are inflated according to their outdegree. Protocols
for anonymity are evolving.
IEN Dataset, July 2010

80. Globalization of Innovation Ecosystems
IEN Dataset, July 2010
Norwegian tech-based
companies with financing
are more likely to have
networked relationships.
Norwegian tech-based
companies have access to
global relationships through
current board members,
investors, and key personnel.

81. Dynamics
IEN Dataset, July 2010
/Users/neil/Documents/neil/Research/Innovation_Ecosystems/proj/Norway/g1-norway/movie/n1

82. Insights About Norway
• Dual offices: regional and Oslo
• In sectors we studied
• Business locations parallel technical university programs
• Investor relationships have strong local links
– Some investing organizations are governmental programs
– Expands to Oslo when offices are in Oslo
– International relationships linked to small set of personal
relationships at executive level
• International investors drawn through executive
relationships
– Relationships through execs at Google and AOL provide
channels for global network expansion

83. FINLAND

84. Case Example: Funding for Finland
xample view to IEN dataset in Gephi. Nodes represent companies and their investors; companies are selected with
eyword search “Finland + Finnish”. The network layout is created with Yifan Hu Multilevel algorithm and nodes are
flated according to their indegree, i.e. the number of the connected investors.

85. Case Example: Funding for Finland
xample view to IEN dataset in NodeXL. Nodes represent companies and their investors; companies are selected with
eyword search “Finland + Finnish”. Nodes are inflated according to their indegree, i.e. the number of investors of a
ompany. Finnish Industry Investment is the main investor with outdegree 17 (betweenness centrality 1965).
Degree distribution

86. Case Example: People & Tampere
Example view to IEN dataset for keyword search “Tampere”. Nodes represent companies and their previous and
current employees. The network layout is created with Fruchterman Reingold algorithm and nodes are inflated
according to their outdegree.

87. University - Industry
PRELIMINARY

92. Future Work:
Untangling the Web, Looking for Patterns

93. http://4.bp.blogspot.com/_qFju91K89HM/SxRpABd1DTI/AAAAAAAABjw/6LaSJfjfk-I/s1600/Unexpected_Guests.jpg
What do we know to ask?
What can we learn that we don’t
know to ask?