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SNA – theories put into practice: Data Mining in Sozialen Netzwerken

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SNA – theories put into practice: Data Mining in Sozialen Netzwerken auf dem 9. Twittwoch zu Berlin am 10. März 2010

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SNA – theories put into practice: Data Mining in Sozialen Netzwerken

  1. 1. sociomantic labs SNA – Theories put into practice !
  2. 2. Agenda 2  Definition & Historie  Was ist möglich & nötig?  Anwendungsbeispiele & Trends  Mitspieler  Ressourcen  Q&A & Kontakt
  3. 3. Definition 3 Social network analysis (SNA) is a quantitative analysis of relationships between individuals or organizations. By quantifying such social structures it is possible to identify most important actors, group formations or equivalent roles of actors within a social network. Kirchhoff, 2010
  4. 4. Historie 4 1933 Mayo relationships in companies 1934 Moreno sociogram 1955 Barnes term „social network“ coined 1985 Granovetter importance of weak ties 1992 Burt Structural holes, Betweenness 1994 Wassermann Social Network Analysis: Methods and Applications & Faust 2002 Dunbar Dunbar number 2003 Watts & Strogatz Small Worlds sociogram - Moreno 1934 2006 Newman, Barabási Structure and Dynamics of networks & Watts
  5. 5. Was ist möglich? 5 Zentralitäten Identifikation von Gruppen Wichtigkeit einer Person  Degree  Identifikation nach k-plex, k-core quantifiziert die soziale Aktivität einer Person Identifikation von Gruppen nach Anzahl von geteilten Verbindungen zwischen Gruppenmitgliedern  Closeness quantifiziert die Wichtigkeit einer Person im Hinblick  Identifikation nach Dichte & auf Informationsverteilungspotential und den Grad der Direktheit von Vernetzungen zu anderen Personen clustering coefficient Identifikation von Gruppen nach Intensität der Beziehungen zwischen Gruppenmitgliedern  Betweeness quantifiziert die Wichtigkeit einer Person im Hinblick auf den Informationsfluss zwischen zwei Gruppen  Identifikation nach Interessen
  6. 6. Was ist möglich? 6 Aufbau und Bereitstellung einer www www Datenbank zu sozialen Beziehungen und Interessen von Personen basierend auf der Analyse verschiedenster, frei zugänglicher Quellen im Netz mit Hilfe von Grid- SocialGraph und Cloud Computing Informationen über Personen, deren Interessen und Verbindungen www www Verbindung zwischen Personen
  7. 7. Was ist möglich? 7  Soziales Kapital  Persönliche Rolle  Größe des Netzwerks  Namen der Kontakte  Stärke der Verbindungen  Persönliche Interessen  Themen  Stärke des Interesses  Liste der Quellen
  8. 8. Was ist nötig? 8  BACKEND!  Datenbank  Rechenleistung  Hans Müller!
  9. 9. Anwendungsbeispiele 9  CRM - Inter-Customer Relations Welche meiner Kunden kennen sich und wer ist der Meinungsführer?  Finanzen – Credit Scoring Wer ist wie Kreditwürdig?  Mediaplanung - Customer Media Footprint Wo sind meine Kunden im Netz vertreten?
  10. 10. Beispiel CRM Analyse 10 Soziale Netzwerk Analyse (SNA) KUNDE + + Kundendaten (CRM) CRM Daten SocialGraph API Daten CRM Daten + Kunde C Kunde D + H Person X1 SocialGraph API Daten Möglicher Multiplikator Potentieller Neukunde A B C X1 C D H X1 ? D E F G H I  Ist noch kein Telekom  Bestellen iPhone Kunde  Bestellt iPhone  Wurden in der  Kann potentiell über  Kennt D, H und X1 Kaufentscheidung von Person C als Neukunde  Ist mögliche Multiplikator Kunde C beeinflusst gewonnen werden Januar Juni November
  11. 11. Beispiel Credit Scoring 11  Analyse des direkten Netzwerks  Abgleich des Netzwerks mit Bestandskundendatenbank  Anpassung des Angebots
  12. 12. Beispiel Customer Media Footprint 12  Wo sind meine Kunden im Netz vertreten?  Analyse des WEB Nutzungsverhaltens nach Kundengruppen  Analyse des sozialen Kapitals
  13. 13. Beispieldatensatz 13 Kunden in in in in Anzahl Durchschnitt in MySpace Facebook XING LinkedIn der Kontakte pro Durchschnitt Anteile Männer Produkt Online Communities (%) (%) (% ) (%) (% ) Kontakte Kunde Alter und Frauen A 60,6 8 33 17 8 112350 112 35,7 89 / 11 B 55,3 5 41 23 11 117221 119 34,1 83 / 17 C 57 12 45 16 9 278450 83 33,6 79 / 21 D 65 14 42 23 11 312723 93 32,9 77 / 23 E 68 15 57 24 13 378283 97 32,3 73 / 27
  14. 14. Trends 14  Breitere Anwendung Level der sozialen Aktivität (SA)  Social Commerce  HR Thematisches  Online Communities Interesse (T)  Information Retrieval Q x SA x T = TKP = Fair value for money!!  Individuelles Targeting Qualität des  Individuelles Pricing Users (Q)
  15. 15. Mitspieler 15
  16. 16. Ressourcen? 16 Social Network Analysis concept Author Network Level Micro Element Level Centrality Bavelas (1950), Sabidussi (1966), Nieminen (1974) Bonacich (1972), Freeman (1979), Freeman (1980), Macro (Degree, Closeness, Betweenness) Knoke & Burt (1983), Bonacich (1987), Friedkin (1991), Borgatti (2005), Burt (1992), Hage & Harary (1995), Marsden (2002), Newman (2003), Latora & Marchiori (2007) Group Level Local Density Barnes (1969), Mitchell (1969), Niemeijer (1973), Barnes (1974) Granovetter (1976), Friedkin (1981), Snijder (1981) Tie Strength Granovetter (1973), Friedkin (1980), Lin, Ensel & Vaughn (1981), Wellman & Wortley (1990) Connectivity Luce (1950), Harary (1969), Krackhardt (1994), Wasserman & Faust (1994) Network Position & Equivalence Lorrain & White (1971), Boorman & White (1976), White, Boorman & Breiger (1976), Sailer & Douglas (1978), DiMaggio & Powell (1983), DiMaggio (1986), Erickson (1988), Galaskiewicz & Burt (1991) Clustering & Connectivity Harary, Norman & Cartwright (1965), Davis (1967), Holland & Leinhardt (1970), Cartwright & Harary (1979), Network Everett, Boyd & Borgatti (1990), Borgatti & Everett Neighborhood (1992), Growth of sociological publications Network Network Statistics (Degree, Distance, Shortest Path, Clustering Mohar (1991), Barabási, Albert & Jeong (1999), indexed with social network in the Coefficient, Transitivity) Kleinberg et al. (1999), Newman, Watts & Strogatz abstract (source: Borgatti & Foster, 2003) Level (2002), Brandes & Erlebach (2005) Network Models Milgram (1967), Newman, Watts & Strogatz (2002), Brandes & Erlebach (2005)
  17. 17. Ressourcen! 17  Literatur Faust & Wassemann, Social Network Analysis: Methods and Applications,1994 Barabási, Linked: How Everything Is Connected to Everything Else and What It, 2000
  18. 18. 18 Q&A
  19. 19. Kontakt 19 Thomas Brandhoff Managing Director mail thomas.brandhoff@sociomantic.com phone +49 (0) 30 50 15 47 01 sociomantic labs GmbH Chausseestraße 16 10115 Berlin

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