1. What Is A Network? (and why do we care?)

2. 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

3. 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 …

4. 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

5. 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

6. Britain From Above (http://www.bbc.co.uk/britainfromabove)http://www.bbc.co.uk/britainfromabove Data Networks

7. 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

8. http://blog.linkedin.com/2011/01/24/linkedin-inmaps/ Human Networks

9. 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

10. Unclustered Network

11. Clustered Network

12. Types of Networks 1. Grid/lattice network (structure, order) 2. Small-world network (a mix of order and randomness) 3. Random network (randomness)

13. Power Law

14. 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

15. 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

16. 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

17. An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 17 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

18. 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

19. 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

20. California: F500 Companies http://flickr.com/photos/11242012@N07/1363558436

21. TheyRule.Net

22. An Introduction to Network Theory | Kyle Findlay | SAMRA 2010 | 22 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

23. 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)RickRollingmeme How might we actively seek new ideas/voices? What is the role of software here? Discussion

24. 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