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What Is A Network?(and why do we care?)
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 2
“A collection of objects (nodes)
connected to each other in some
fashion” - Watts, 2002
Network Defined
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 3
• An agent/object's actions are
affected by the actions of
others around it.
•Actions/choices are not made in
isolation, i.e., they are contingent on the actions and choices of others
In A Network …
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 4
The Internet
Neural Networks (computer & human)
Proteins and Genes
Stem cells (and other cells)
Diseases
Social Groups
Examples of Networks
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 5
• Theory-> from “fixed” to “dynamic”
• “real networks represent populations of
individual components that are actually
doing something” - Watts, 2002
• Networks are key to understanding non-
linear, dynamic systems
Networks
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Britain From Above (http://www.bbc.co.uk/britainfromabove)
Data Networks
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Terms
• Node = individual components of a network, e.g. people, power stations, links• Edge = direct link between components (referred to as a dyad in context of social networks, a relationship between two people)• Path = route taken to connect two nodes. “Six degrees of separation” average path length = 6
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http://blog.linkedin.com/2011/01/24/linkedin-inmaps/
Human Networks
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 9
• Began in 1967 at Harvard University• Sent packages to randomly selected people in
Omaha, Nebraska & Wichita and asked that they
be delivered to individuals in Boston,
Massachusetts• Could only forward to people they knew on a first-
name basis• 64 of 296 letters reached their destination• Average path length of these was around 5.5 or 6
Milgram’s Experiment
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Unclustered Network
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Clustered Network
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Types of Networks
1. Grid/lattice network(structure, order)
2. Small-world network(a mix of order and randomness)
3. Random network(randomness)
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Power Law
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 14
• Power law distributions tend to arise in social systems where many people express their preferences among many options. • As the number of options rise, the curve becomes more extreme.• Most elements in a power law system are below average (the “long tail”)
Shirky On Power Law
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 15
“Alice, the first user, chooses her blogs unaffected by anyone else, but Bob has a slightly higher chance of liking Alice's blogs than the others. When Bob is done, any blog that both he and Alice like has a higher chance of being picked by Carmen, and so on, with a small number of blogs becoming increasingly likely to be chosen in the future because they were chosen in the past.”
Shirky On Power Law
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 16
“Within a social network, weak ties
…are indispensable to
individuals’ opportunities
and to their incorporation
into communities while strong
ties breed local cohesion.”(Mark Granovetter, 1973)
Networks : Weak Ties
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• The stronger the tie between two people, the more similar they are, in various ways (Mark Granovetter, 1973)
• Weak ties = “friends of friends” • Weak ties provide a bridge between social circles, access to information and resources beyond my “tight” social circle
Networks : Weak Ties
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 18
“On average, the first 5 random
re-wirings reduce the average
path length of the network by
one-half, regardless of the size
of the network” [Watts, 2002]
Weak Ties Are Powerful
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An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 19
Over time, we are more likely to
become acquainted if we have
something in common• this bias towards the familiar reduces the
pure randomness of connections• “homophily” or “birds of a feather flock
together”
Strong Tie Truism
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California: F500 Companies
http://flickr.com/photos/11242012@N07/1363558436
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TheyRule.Net
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• Large scale• Continual growth• Distributed, organic growth: vertices “decide” who to link to• A mixture of local and long-distance connections• Interaction (largely) restricted to links• Abstract notions of distance: geographical, content, social…
Informal Properties : The Web
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• Reaction to “your friends have more friends
than you do” (friends paradox - TED talk)
• What does a highly “spreadable”
(viral) idea look like? (RickRolling as a meme)
• How might we actively seek new ideas/voices?
What is the role of software here?
Discussion
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Credits
• Kathy E Gill, @kegill, CC share-share-alike, non-commercial• Sources:
– Duncan Watts, Six Degrees of Separation– Kyle Findlay, SAMRA 2010 Conference presentation– Michael Kearns, Social Network Theory