4.0 social network analysis


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4.0 social network analysis

  1. 1. Social Network Analysis
  2. 2. What is social networks?
  3. 3. > Who are disconnectedwith most of people?> Who do play the“connector” role?> Sub-communities?> Central role?Only facebook friendsMost of “my friends” are my facebook friendsGenerated by TouchGraph
  4. 4. “Networks are sets of nodesconnected by edges”NetworkmathCSphysicssociologypoints linesvertices edges, arcsnodes linkssites bondsactors ties, relation= GraphPaperAuthorInstituteJournalCitingCo-author
  5. 5. Examples
  6. 6. Wang, Xiao Fan, and Guanrong Chen. "Complex networks: small-world, scale-free and beyond." Circuits andSystems Magazine, IEEE 3.1 (2003): 6-20.
  7. 7. Sociology -Social network theoriesApplied Physics -Small WorldApplied Physics -Chaos, complexitySociology -Simulationhttp://www.touchgraph.com/amazon
  8. 8. “Injects drugs with” relation
  9. 9. Keyword networks of news: Cancer
  10. 10. Keyword network of news: flu
  11. 11. Keyword network of news:
  12. 12. http://ir.itc.ntnu.edu.tw/udn/search.aspx
  13. 13. •G+, HIV positive gay man•G−, HIV negative gay man•G?, gay man, unknown HIV•NG+, HIV positive injecting drugusing (N = needles) gay man•NG−, HIV negative injecting drugusing gay man (n = 4);•NG?, injecting drug using gayman, unknown HIV status (n = 1);•F−, HIV negative woman (n = 5);•F?, woman, unknown HIV status (n= 7);•NF+, injecting drug using HIVpositive woman (n = 1);•NF?, injecting drug using woman,unknown HIV status (n = 1);•M−, HIV negative heterosexualman (n = 1); M?, heterosexualman, unknown HIV status (n = 3);•NM+, injecting drug using HIVpositive man (n = 1);•NM?, injecting drug using man,unknown HIV status (n = 1).Potterat, et al. Risk network structure in the early epidemic phase of HIV transmission in Colorado Springs, SexuallyTransmitted Infections 78, i159–i163 (2002).•G: gay man•F: woman•M: heterosexual man•+/-/?: HIV status•N: Injecting drug usingContact-tracing network of sexual and injecting drug partners from 1985-1999 in Colorado Spring
  14. 14. Potterat, et al. Risk network structure in the early epidemic phase of HIV transmission in Colorado Springs,Sexually Transmitted Infections 78, i159–i163 (2002).Visualized by Newman, Mark EJ. "The structure and function of complex networks."SIAM review 45.2 (2003): 167-256.
  15. 15. • Understanding the structure of sexualnetworks is critical for modelingdisease transmission dynamics, ifdisease is spread via sexual contact.• This project describes the structureof an adolescent sexual networkamong a population of over 800adolescents residing in a mid-sizedtown in the mid-western US.• Real sexual and romantic networksare characterized by long contactchains and few cycles.• Implications for disease transmissiondynamics and social policy areexplored."Data drawn from Peter S. Bearman, James Moody, and KatherineStovel, Chains of Affection, American Journal of Sociology 110, 44-91(2004)High-school dating network
  16. 16. Contact tracing network of TuberculosisContagion of TB, books on politics:Valdis Krebs, www.orgnet.com.• Black: super-spreaders• Pink: infectious• Green: susceptible• Gray: uncertain
  17. 17. Role-interaction networks of movie “Les Miserables”M. E. J. Newman and M. Girvan, Finding and evaluating community structure in networks, Physical Review E 69, 026113 (2004).
  18. 18. InterdisciplinarycollaborationsM. Girvan and M. E. J. Newman, Community structure insocial and biological networks, Proc. Natl.Acad. Sci. USA 99,8271-8276 (2002).
  19. 19. Why social Network?
  20. 20. Who will be the most lonely one? With thefewest social support.Exclusion mechanism: ca and e will spend most of their time on b and d, since eachof them only has one friend.
  21. 21. Which central role can spread rumor efficiently?Binding mechanism: Closed one.The central point in a closed network cannot spread rumor easily,because its friends will double-check what it said.It is alsoeasier to be attacked by rumor.
  22. 22. Compared with Ke,Lan, and Hang, Chen-Yi shared fewerfriends with me.However, she behavedas a connector whobridge severalcommunities of myfriendship.Generated by TouchGraph
  23. 23. •RobustnessMeasurement - Clustering Coefficient•EfficiencyMeasurement - Degree of Separation
  24. 24. 1) 2)robustness EfficiencyAccuracy = Robustness Speed = Efficiency
  25. 25. TB#ContactRegularBT0like Sexual#contactDecentralizedDecentralized Centralized CentralizedGated GatedGatedSuper0SpreaderSuper0SpreaderRobustness? Efficiency?short0distancelong0distancelong0distanceshort0distancelow0clusteringlow0clusteringhigh0clusteringhigh0clustering
  26. 26. A: Depending on “what” propagate on the networkInfectious diseasesDoomsday predictionsAnti-nuclearHealth InformationComputer virusesMovie recommendationsQ: Is high efficiency(robustness) good or not?
  27. 27. • What-is and why network analysis‣ structure’s effect rather than individual traits• Structure’s quantitative patterns‣ e.g., separation, clustering, degree distribution• Why and how to become to a network‣ Why: demographic-reasons‣ How: considering micro-pattern: network formation• Verify the casual relations between structure and outcome‣ Modeling, or statistical inferenceReview
  28. 28. Scale-Free: low clustered but highly-skewed degree distributionLow degree of separationHigh degree of clusteringWang, Xiao Fan, and Guanrong Chen. "Complex networks: small-world, scale-free and beyond." Circuits and Systems Magazine, IEEE 3.1 (2003): 6-20.
  29. 29. History & DevelopmentFreeman, Linton C. The development of social network analysis: A study in the sociology ofscience. Vol. 1. Vancouver: Empirical Press, 2004.
  30. 30. http://oracleofbacon.org/
  31. 31. • The four largest circles representcottages in which the girls lived.• Each of the circles within the cottagesrepresents an individual girl.• The 14 runaways are identified byinitials (e.g., HIL).• All non-directed lines between a pairof individuals represent feelings ofmutual attraction.• Directed lines represent one-wayfeelings of attraction.J.#L.#Moreno,#Who#Shall#Survive?#Nervous#and#Mental#Disease#Publishing#Company,#Washington,#DC,#1934)Moreno’s Sociogram of girls in cottagesGirls; runaway are related to their social network
  32. 32. Girls’ school dormitory dining-table partners, 1st and 2nd choices(Moreno, The sociometry reader, 1960)
  33. 33. • Fischer(1948). Does the result of urbanization, in new cities,destroy community? Q: What does the “community” mean?‣ By investigating relationship between individuals, love, hate, support, and so on.‣ 1050 adults living in 50 northern Californian communities‣ Urbanism really reduces network density.‣ Urbanism is negative related to psychological satisfaction and overall well-being.• Hollingshead (1949). The adolescents’ behaviors werestrongly influenced by the “cliques” to which they belonged.
  34. 34. • Coleman(1966) Rogers (1995)• Granovetter(1973)Strong ties among familiar contacts increaseredundant information.• Burt (1992)Social capital: How contacts are connected to each other can influencehow individuals access resources• Krackhardt (1992)• (1981)
  35. 35. e hope to know...• Are nodes connected? and how?Can you connect to someone?• How far apart are they?How many stepor how much effort do you need to get someone?• How does a node situate onnetwork? Its importanceWho should you connect firstly?• Is the network composed ofcommunities?How do they cluster together?ConnectedReachableDistanceCentralitysuper-spreaderSub-componentsBelonging
  36. 36. Backup
  37. 37. Keywords(We have)relationshipembedednessclustering coefficient, separationcentralitycloseness, betweennessstructure balancecliques, community, homophilyweak tie, strong tiesocial capital, structural holeNetwork modelsmall world network, scale-free networkrandom network