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Social Network Analysis Current Developments

Social Network Analysis Current Developments. Martin Everett Mitchell Centre for SNA University of Manchester. The rise and rise of SNA. Exponential increase in papers published Expanding fields of application In the period1969-74…20 papers In the period 2004-09..13,600.

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Social Network Analysis Current Developments

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  1. Social Network AnalysisCurrent Developments Martin Everett Mitchell Centre for SNA University of Manchester

  2. The rise and rise of SNA • Exponential increase in papers published • Expanding fields of application • In the period1969-74…20 papers • In the period 2004-09..13,600

  3. Traditional methods • Centrality • Cohesive subgroups • Cohesion • Positional Analysis • Triadic methods • Statistical models (static) • Visualization

  4. Traditional Applications • Small bounded groups • Organizations • Small affiliation data • Social support (Ego network studies) • Social Biology

  5. Traditional Practioners • Anthropologists • Sociologists • Social work • Psychologists • Organizational theorists

  6. The new networkers • Physicists • Mathematicians/Statisticians • Economists • Computer Scientists • Biologists • Ecologists • Criminologists • Health Professionals

  7. New areas • Terrorist networks • Criminal networks • Economic networks • WWW and internet • Social networking data • Small world • Dynamic networks • Large and complex networks • Network formation

  8. Network Science “The study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena." American National Research Council • 2004-2009 4,410 articles

  9. Established Trends INSNA sunbelt workshops give established trends. Until 2008 there were usually 3 standard workshops streams plus one guest • Intro (non technical) • Software • Statistical

  10. In 2010 • 16 workshops (15 in 2009) • 3 introductory • 3 applications (Econ, text analysis, Public health) • 4 Data Orientated (ego,2-mode/valued, collection) • 3 Visualization • 2 Statistical (Dynamic, modelling)

  11. Specialist Conferences • Variety of Professional conferences aimed at practioners • Neo Metrics • IRE (journalists) • The network thinker (Valdis Krebs) • Academic • 2 mode data • Dynamic • Criminal networks

  12. Trends and Potential trends • Dynamic Networks • Network formation • Visualization • 2-mode networks • Mixed methods • Small world networks and other typologies • Large networks • On line networks

  13. More trends • Animal networks • Covert networks • Economic • Public Health/ Epidemiology • Networks and spacial data

  14. Two examples Trajectories Work developed by Steve P Borgatti and D Halgin at University of Kentucky This work presented at the 2-mode conference in Amsterdam in the Autumn. Slides available on the Web.

  15. Social trajectories Film Cast Blue dots are actors First 13 films of Pedro Almodóvar Actor by film incidence matrix Films identified by chronological order

  16. Director: Almodovar 1.50 SaLajusticia AlCasanova AnAlonso PeCoyote AuGirard AlMayo RySakamoto MaOWisiedo AnLizaran EnPosner CrMarcos FeAtkine MiGomez ViAbril AVGomez JeFerrero AsSerna EvCobo 1.00 Film10 VeForque BiAndersen NaMartinez Film9 Film5 LuHostalot EuPoncela BeBonezi JuMArtinez GoSuarez LuBriales AALopez AnBanderas EnMorricone MGRomero LoCardona LoLeon Film4 AnLlorens TaVillalba maBarranco JLAlcaine MAPCAmpos MaVelasco MiMolina Film8 GuMontesinos RdPalma Film6 ChLampreave 0.50 LiCanalejas CrPascual MaCarillo MaZarzo LuCalvo Film7 Film3 CaMaura ALFernandez EsGarcia AgAlmodovar PeAlmodovar PeCoromina JoSalcedo MaParedes JuSerrano 0.00 KiManver Film11 JuEchanove CaElias MaVargas HeLine -0.50 ImArias AlIglesias Film13 AfBeato -1.00 Film2 RMSarda Film12 FFGomez ASJuan CaPena MiRuben FeGuillen Film1 OfAngelica PeCruz AnSantana FeVivanco AgAlcazar CeRoth MaMuro JaBardem JoSancho Pibardem AnMolina AlAngulo LiRabal FrNeri CoGregori PaDelgado -1.50 AlaskaPegam FrFemenias FeRotaeta EsRambal OGAlaska PaPoch EvSilva -2.00 -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 2.00 Correspondence analysis

  17. Idealized pattern 3.28 1 16 2.73 2.18 2 15 1.63 FILM1 FILM10 3 14 1.09 FILM2 FILM9 4 13 0.54 5 12 -0.01 6 11 FILM3 FILM8 7 10 -0.56 FILM4 FILM7 FILM5 FILM6 8 9 -1.11 -1.66 Simulation results Each successive film carries over a number of actors from previous film, and adds new ones -2.21 -2.21 -1.11 -0.01 1.09 2.18 3.28

  18. Director: Garci JCarballino 1.50 AFerrandis AGonzalez MMFernandez PInfanzon JPachelbel ALlorente CPorter JMFernandez PSerrador TGimpera SCanada SAmon JCueto EHoyo VVera EPaso JPuente 1.00 Film8 Film5 RHernandez AMarsillach VValverde DSalcedo ESuarez PCalot YRios PHoyo NGarci Film7 Film10 ECerezo JCalot AGonzalez AValero RVillascastin MSampietro MRMartinez ABSanchez ACarbonell LdOrduna BSantana MEFlores DAguado FGuillen EAsensi CGCuervo RPCubero CGConde ARozas JBodalo JCarideFAlgora FGuillenCuervo LMDelgado FFGomez RAlonso VPanero FPiquer JCaride CCruz JGCaba 0.50 JGluck MLorenzo NRodriguez LBosch JLMerino Film12 Film11 Film6 ECohen MMerchante AFernandez RdPenagos MRellan FBilbao JYepes JMCervino HValcarcel MRojas Film2 0.00 Film4 MoWisiedo MBlasco MTejada MRellan EFornet RFraile FVidal CRodriguez GCobos MGSinde JLGarci Film1 FFaltoyano JSacristan CCadenas AGamero STortosa SAndreu HAlterio Berta MCasanova MFraguas MMassip MBalboa -0.50 -1.00 ALanda -1.50 -2.00 Film9 Film3 -2.50 MMorales VMataix FFaltoyano CGomez ALarranaga CJimenez OLorente FArribas RTebar CLarranaga DPenalver MVerdu APicazo MLPonte ICGutierrez -3.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 2.00

  19. Network Formation • Adapt preferential attachment • Actors attach to high degree others with similar attributes • Use a single number to model attributes • Add an edge with probability proportional to some function of degree and similarity of attribute

  20. Adding in constraint • Networks develop bounded by constraint • There are a small number of seemingly random connections in the whole population • Simulate a number of randomly sized networks then add in a few random connections.

  21. Example • 5 c/p simulation graphs of random size drawn from 0 to 1000 actors • 5 networks of size 318,66,222,153,252 • Merged into a network of 1011 actors • Random connections added at 0.02%,0.002% and 0.0002% Approx 2000, 200 and 20 random edges

  22. Conclusion • SNA here to stay • Many new applications and application areas • Dynamics and simulation now an integral part of research • Moving out of academia into the business community

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