Social Networks as a Foundation for Computer Science

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Social Networksas a Foundationfor Computer
Science

• Owen Astrachan, Jeff Forbes, Susan Rodger,
• Casey Alt, Richard Lucic, Robert Duvall
• http//www.cs.duke.edu/csed/harambenet

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Where are we going Questions

• What should our concerns be for those choosing to
  major in Computer Science?
• courses, research, jobs,
• Should we be concerned by the precipitous decline
  in those taking our courses or majoring or ?
• majors, technical students, non-technical
• What can we do to ensure the ongoing success of
  our academic discipline?
• Look inward, look to others

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Acknowledgements

• Social Networks/Broadening Participation group
• Jeff Forbes helped with this talk
• Casey Alt, Richard Lucic, Susan Rodger
• Students Ben Spain Dametrious Peyton
• Drawn from the work of
• Michael Kearns, UPenn
• Eytan Adar, formerly of HP Labs
• John Breese, David Heckerman, Microsoft Research
• Jonathan Herlocker, Oregon State University
• Thomas Hoffman, Brown University
• Marti Hearst, UC Berkeley
• Jennifer Golbeck, University of Maryland

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WWDD?
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Questions

• If you gotta ask, youll never know
• Louis Armstrong Whats jazz?
• If you gotta ask, you aint got it
• Fats Waller Whats rhythm?
• What
  questions did you ask in school
• today?
• Arno Penzias via Isaac Isadore Rabi

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Questions and Answers

• Judge a man by his questions rather than by his
  answers
• 
  Voltaire

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Computer Science

• What is the foundation of computer science?
• Historically, now, in the future
• What changes are here, on the horizon?
• From theory to practice to education
• Can we relate to what and how students learn?
• Is every generation different, the same,

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History and Computer Science

• Those who cannot remember the past are condemned
  to repeat it.
• Dont know much about history, dont know much
  about biology, dont know much about a science
  book

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Who, when?

• No stretching is required to envision computer
  consoles installed in every home.
• Everyone will have better access to the Library
  of Congress than the librarian himself now has.
• Full reports on current events, whether baseball
  scores, the smog index in Los Angeles or the
  minutes of the 178th Korean Truce Commission will
  be available for the asking.

• John, McCarthy, Information, Chapter 1, 1966

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You win some, you lose some

• People will soon become discontented with the
  canned programs available they will want to
  write their own. The ability to write a computer
  program will be as widespread as the ability to
  drive a car.
• Not knowing how to program will be like living in
  a house full of servants and not speaking their
  language.
• Many people can write simple programs after an
  hour or two of instruction. Programming is far
  easier to learn than a foreign language or
  algebra.

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Then and Now
kentlew.com

• Bailey, SIGCSE 1972
• It is remarkable that the majority of students
  can indeed handle fairly complex (Fortran) I/O by
  the end of the first six lessons, even though
  they have not actually been formally taught how
  to do it.
• Roberts et al, SIGCSE 2006
• The problem most often cited by those attempting
  to teach Java to novices is the lack of a simple
  input mechanism,

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What has changed in 20 years?

• Machines
• Characteristics and Availability
• Internet
• Availability, IM, web, Google,
• Students
• Comfort with technology, Expectations

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Teaching Compsci in 1984

• 64K memory, 128K extended
• 8-bit, 1 Mhz 6502 processor
• 5Mb drive 3500
• UCSD Pascal gt100
• Owen's machine 3000
• 677.80 in 1984 has 1200 "purchase power" in
  2003
• http//eh.net/hmit/ppowerusd/

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Typical machine in 2006?

• 1 Gb memory
• 3 GHz, 32-bit chip
• Cache,
• 160 Gb disk
• Lots of free resources
• Good academic pricing
• Under 600 (priced 6/19/06)

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The more things change?

• Assume I took your first course(s) in 1984 and
  understood the concepts so completely that I
  could still get a 100 on the final from 1984 if I
  took it today (e.g., I've been in a cryogenic
  chamber). How would I do on the 2004 final exam?

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What has changed in Physics?

• "You'd get a 100 plus or minus sigma. Intro
  classical physics hasn't really changed that much
  over the last 100 years. In graduate level e.g.
  EM or quantum classes I think ditto, although
  sigma would be bigger (and might depend more on
  the instructor variation than on any real
  variation in the material). The main difference
  is, I think, that your chances of GETTING 100 now
  would be much higher."
• Rob Brown,
• Poohbah of Physics Instruction

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What has changed in Biology?

• "The basic principles and concepts of biology
  haven't changed much in 20 years.  What has
  changed relates to specific content, and in this
  arena the changes have been enormous.  20 years
  ago, we barely knew how to sequence DNA today
  information of this kind has had a major impact
  on just about every topic in the biological
  sciences.  Thus, some questions on an exam today
  would address topics that would be completely
  unfamiliar to a 1984 time-traveller. "
• Greg Wray,
• Director of Undergraduate Studies, Biology

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What has changed in Economics?

• " we now cover material that was only introduced
  in an advanced or intermediate course in 1984. In
  1984 we spent the bulk of the time dealing with
  the Keynesian model and virtually no dialogue
  about supply side policies. Now the Keynesian
  stuff is a small subset of a much broader
  exposure to Aggregate demand and supply Also
  there is more international coverage now - as
  opposed to 20 years ago for obvious reasons."
• Lori Leachman,
• Director of Undergraduate Studies, Economics

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What has changed in Calculus?

• We have two varieties of calculus courses, the
  lab courses and the traditional ...  The latter
  two have not changed significantly in decades,
  and I think that a student who fared well on the
  1984 exam in those courses would do well today,
  and vice versa.
• In the lab courses You would ace about half the
  exam.  The other half would be unfamiliar to
  you.  For example, you would probably not know
  how to answer a problem on modeling a set of
  data, creating an approximation using Euler's
  method, interpreting derivatives in the context
  of applications in other fields, or giving
  explanations of ideas
• Lewis Blake,
• Supervisor of First-year Instruction

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Changes in Computer Science?
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Changing CS? Rock, Hard place

• If Computer Science has changed drastically is it
  to keep up with fads and stylistic changes or
  because of fundamental changes in the discipline?
• Are we leveraging the technological and
  intellectual resources at our disposal
• If we havent changed, is it because of a solid
  bedrock of principles that endures? Or because
  were lazy, good-for-nothing,

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What is CS? Who wants to study it? Why do they
want to?
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NYTimes in 1984
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What is CS? Why study it?

• Do we have Physics (Math, ) Envy?
• It's hard for voice over Internet Protocol or
  e-commerce to compete with finding the age of the
  universe, Peter Lee, CMU
• Does familiarity breed contempt?
• What was different in 1984 than today?

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What is Computer Science?
What is the central core of the subject? What
is it that distinguishes it from the separate
subjects with which it is related?

• What is the linking thread which gathers these
  disparate branches into a single discipline? My
  answer to these questions is simple --- it is the
  art of programming a computer.

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Is this Computer Science?

• public static void stuff(int n)
• doit(n,0,1,2)
• public static void
• doit(int n,int f, int t, int a)
• if (n 1) move(n,f,t)
• else
• doit(n-1,f,a,t)
• move(n,f,t)
• doit(n-1,a,t,f)

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Occupational Distribution of Projected SE Job
Openings 2002-2012
John Sargent, US Department of Commerce, 2004
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Annual Degrees and Job Openings in Broad SE
Fields

John Sargent, US Department of Commerce, 2004
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Demographics 18 - 24 year olds
White Asian/ Pacific Islander Hispanic African American Native American
2000 66 4 15 14 1
2010 63 5 17 14 1
2025 55 7 22 15 1
US Census Bureau
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Bachelors Degrees from Doctoral Institutions
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COHFE

• Amherst College, Barnard College, Brown
  University, Bryn Mawr College, Carleton College,
  Columbia University, Cornell University,
  Dartmouth College, Duke University, Georgetown
  University, Harvard University, Johns Hopkins
  University, Massachusetts Institute of
  Technology, Mount Holyoke College, Northwestern
  University, Oberlin College, Pomona College,
  Princeton University, Rice University, Smith
  College, Stanford University, Swarthmore College,
  Trinity College, University of Chicago,
  University of Pennsylvania, University of
  Rochester, Washington University in St. Louis,
  Wellesley College, Wesleyan University, Williams
  College, Yale University

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If you dont take a course in CS, you wont major
in it.

• Why is the first year different from all other
  years?

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Who's going to College?
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Who's going to College?
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Who's going to College?
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How do we get Studentsinto the Compsci Tent?

• Why is the first year different from all other
  years?

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Interdisciplinary minors

• At Duke it is difficult to double major in
  sciences
• Too many requirements, 17 courses in biology
• Students are interested in credentials
• No business major/minor, certificate program
  (requires intro, capstone, six courses)
• Minor requires five courses, double counting ok
• Three courses in CS, two in econ or biology
• From gene to social networks, data mining,

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Genome Revolution Focus Course

• Arts in Contemporay Society, Exploring the Mind,
  Evolution and Humankind, 20th Century Europe,
  Visions of Freedom, The Genome Revolution and its
  Impact on Society,
• Three of four courses, one writing, two others.
  Interdisciplinary 0.5 credit seminar P/F
• Seminars, students live in same dorm
• 600 out of 1600 in FOCUS course
• For Genome, 80 applicants for 30 slots, 65 women
• In CS Genomics course 8 women, 9 men

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Simple examples

• Given strand of DNA, calculate CG ratio
• Potential source of proteins CGGATTATC
• Given protein HLVWW calculate number of
  different DNA strands that could code for it
• 64 codons, 20 amino acids
• Find heaviest protein in array of proteins
• Given atomic mass of amino acids
• Interpret ORF data from NCBI website

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From Algorithms to Objects

• Read DNA assumed to be in 5 to 3 orientation
• Use BioJava to read via http
• Construct reverse complement (3 to 5)
• From CAATT produce AATTG
• How big is the human genome?
• Runtime of algorithm O(1)

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Computer Science is filled with real-world
examples.

• Why is the first year different from all other
  years?

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Teaching as

• English is not history and history is not
  science and science is not art and art is not
  music, and art and music are minor subjects and
  English, history and science major subjects, and
  a subject is something you 'take' and when you
  have taken it, you have 'had' it, and if you have
  'had' it, you are immune and need not take it
  again." (The Vaccination Theory of Education?)

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Back to the Future

• How will we know when we get there?

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A Future for Computer Science?
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Is there a Science of Networks?

• From Erdos numbers to random graphs to Internet
• From FOAF to Selfish Routing apparent
  similarities between many human and technological
  systems organization
• Modeling, simulation, and hypotheses
• Compelling concepts
• Metaphor of viral spread
• Properties of connectivity has qualitative and
  quantitative effects
• Computer Science?
• From the facebook to tomogravity
• How do we model networks, measure them, and
  reason about them?
• What mathematics is necessary?
• Will the real-world intrude?

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Physical Networks

• The Internet
• Vertices Routers
• Edges Physical connections
• Another layer of abstraction
• Vertices Autonomous systems
• Edges peering agreements
• Both a physical and business network
• Other examples
• US Power Grid
• Interdependence and August 2003 blackout

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What does the Internet look like?
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US Power Grid
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Business Economic Networks

• Example eBay bidding
• vertices eBay users
• links represent bidder-seller or buyer-seller
• fraud detection bidding rings
• Example corporate boards
• vertices corporations
• links between companies that share a board
  member
• Example corporate partnerships
• vertices corporations
• links represent formal joint ventures
• Example goods exchange networks
• vertices buyers and sellers of commodities
• links represent permissible transactions

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Content Networks

• Example Document similarity
• Vertices documents on web
• Edges Weights defined by similarity
• See TouchGraph GoogleBrowser
• Conceptual network thesaurus
• Vertices words
• Edges synonym relationships

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Enron
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Social networks

• Example Acquaintanceship networks
• vertices people in the world
• links have met in person and know last names
• hard to measure
• Example scientific collaboration
• vertices math and computer science researchers
• links between coauthors on a published paper
• Erdos numbers distance to Paul Erdos
• Erdos was definitely a hub or connector had 507
  coauthors
• How do we navigate in such networks?

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Acquaintanceship more
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Network Models (Barabasi)

• Differences between Internet, Kazaa, Chord
• Building, modeling, predicting
• Static networks, Dynamic networks
• Modeling and simulation
• Random and Scale-free
• Implications?
• Structure and Evolution
• Modeling via Touchgraph

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Web-based social networks

• http//trust.mindswap.org
• Myspace 73,000,000
• Passion.com 23,000,000
• Friendster 21,000,000
• Black Planet 17,000,000
• Facebook 8,000,000
• Whos using these, what are they doing, how often
  are they doing it, why are they doing it?

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Golbecks Criteria

• Accessible over the web via a browser
• Users explicitly state relationships
• Not mined or inferred
• Relationships visible and browsable by others
• Reasons?
• Support for users to make connections
• Simple HTML pages dont suffice

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CSE 112, Networked Life (UPenn)

• Find the person in Facebook with the most friends
• Document your process
• Find the person with the fewest friends
• What does this mean?
• Search for profiles with some phrase that yields
  30-100 matches
• Graph degrees/friends, what is distribution?

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CompSci 1 Overview CS0

• Audioscrobbler and last.fm
• Collaborative filtering
• What is a neighbor?
• What is the network?

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What can we do with real data?

• How do we find a graphs diameter?
• This is the maximal shortest path between any
  pair of vertices
• Can we do this in big graphs?
• What is the center of a graph?
• From rumor mills to terrorists
• How is this related to diameter?
• Demo GUESS (as augmented at Duke)
• IM data, Audioscrobbler data

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Collaborative Filtering

• Goal predict the utility of an item to a
  particular user based on a database of user
  profiles
• User profiles contain user preference information
• Preference may be explicit or implicit
• Explicit means that a user votes explicitly on
  some scale
• Implicit means that the system interprets user
  behavior or selections to impute a vote
• Problems
• Missing data voting is neither complete nor
  uniform
• Preferences may change over time
• Interface issues

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My recommendations at Amazon
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And again
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Finally,
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Whose recommendations?
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And again
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Alan Kay

• "Simple things should be simple. Complex things
  should be possible".
• "The best way to predict the future is to invent
  it"