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Networks and connections <ul><li>An introduction to theories of complex networks </li></ul><ul><li>John Brissenden </li></...
@
Reading Barabasi, A-L, and Bonabeau, E (2003), Scale-Free Networks.  Scientific American, May 2003 Benkler (2006), chapter...
Learning outcomes To understand the non-random characteristics of complex networks To apply theoretical models to the www ...
 
Random and scale-free networks
Normal and power-law distribution Benkler (2006): 244
@
Barabasi and Albert (1999) The probability that any node on the network will be very highly connected to many others is VE...
Huberman and Adamic (1999) Each website has an intrinsically different growth rate New sites are formed at an exponential ...
 
black: opinion leaders red : influenced  green : uninfluenced  grey : undecided Viral marketing http://www.orgnet.com Hubs...
Implications The more popular you are, the more popular you become Niches are important Older nodes (sites) tend to be mor...
Clustering Sites cluster into densely-linked regions or communities of interest They link much more to each other than to ...
28%: heavily interlinked: multiple redundant paths 22%: link to core, but not from core; new, or lower-interest sites 22%:...
Benkler (2006): 252 Distribution becomes more normal in smaller clusters
Summary “ Bow tie” model repeats itself within clusters As clusters become smaller, attention is more evenly spread Very v...
The small worlds effect
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Networks

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Networks

  1. 1. Networks and connections <ul><li>An introduction to theories of complex networks </li></ul><ul><li>John Brissenden </li></ul><ul><li>15.12.09 </li></ul>
  2. 2. @
  3. 3. Reading Barabasi, A-L, and Bonabeau, E (2003), Scale-Free Networks. Scientific American, May 2003 Benkler (2006), chapter 7 Terranova (2004), chapter 2 http://www.barabasilab.com/index.php
  4. 4. Learning outcomes To understand the non-random characteristics of complex networks To apply theoretical models to the www and to social networks To consider implications for public relations
  5. 6. Random and scale-free networks
  6. 7. Normal and power-law distribution Benkler (2006): 244
  7. 8. @
  8. 9. Barabasi and Albert (1999) The probability that any node on the network will be very highly connected to many others is VERY LOW The probability that a very large number of nodes will be connected very loosely or not at all is VERY HIGH Preferential attachment: new nodes prefer to attach to well-attached nodes
  9. 10. Huberman and Adamic (1999) Each website has an intrinsically different growth rate New sites are formed at an exponential rate PREFERENTIAL ATTACHMENT + GROWTH = ?
  10. 12. black: opinion leaders red : influenced green : uninfluenced grey : undecided Viral marketing http://www.orgnet.com Hubs: ‘ broadcast’ weakly infectious viruses, ideas
  11. 13. Implications The more popular you are, the more popular you become Niches are important Older nodes (sites) tend to be more popular than new ones, but only on average Money alone is not enough to guarantee future popularity or growth, but relevance and connection to already popular nodes can be
  12. 14. Clustering Sites cluster into densely-linked regions or communities of interest They link much more to each other than to nodes outside Clustering increases and intensifies as you move along the “long tail”
  13. 15. 28%: heavily interlinked: multiple redundant paths 22%: link to core, but not from core; new, or lower-interest sites 22%: link from core, but not to core; doc depositories or internal org sites 22%: cannot reach or be reached from core 10%: entirely isolated Benkler (2006): 248-9
  14. 16. Benkler (2006): 252 Distribution becomes more normal in smaller clusters
  15. 17. Summary “ Bow tie” model repeats itself within clusters As clusters become smaller, attention is more evenly spread Very very few are receiving no attention at all
  16. 18. The small worlds effect

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