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Web Science Course 2014 - Lecture : Social Networks - *

Web Science Course 2014 - Lecture : Social Networks - *. Dr. Stefan Siersdorfer. * Figures from Easley and Kleinberg 2010 ( http://www.cs.cornell.edu/home/kleinber/networks-book /). What is a Social Network ? . Entities ( persons , companies , organizations )

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Web Science Course 2014 - Lecture : Social Networks - *

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  1. Web Science Course 2014- Lecture: Social Networks - * Dr. Stefan Siersdorfer * Figuresfrom Easley and Kleinberg 2010 (http://www.cs.cornell.edu/home/kleinber/networks-book/)

  2. Whatis a Social Network ? • Entities (persons, companies, organizations) • Connections betweenentities (friendship, collaboration)

  3. ExamplesofSocial Networks • „Real World“ relationshipsbetween people (friends, colleagues, relatives, …) • Online Networks: Facebook, Flickr, Twitter … • Trading Networks betweencompaniesor countries • Collaborationsandrivalriesbeweenpersons, organizations, and countries • Extension: Technological Networks (WWW, Road Networks, Power Grids, ...)

  4. Example 1: Karate Club

  5. Example 2: Communication in Organization (HP)

  6. Example 3: Trade between Countries

  7. Example 4: Medieval Trading in Europe

  8. Example 5: World Wide Web (Blogs on Presidental Election in 2004)

  9. Research Questions • How do socialnetworks form andhowcanwemodelthestructureofSocial Networks? • Howdoesinformationandinnovationpropagate in Social Networks? • How do diseasespropagate in Social Networks? • Howdoestradeandbuisinesswork in Social Networks? • HowtodetectcommunitieswithinSocial Networks? • ….

  10. Topics ofthisLecture • Homophilyand Segregation • FriendsandFoes • The Small World Phenomenon

  11. PART I:Homophily and Segregation

  12. Properties of Nodes and Homophily • Properties: age, gender, education, location, profession, political opinion, … • Homophily: Similar nodes are more likely to form links. • Reasons for homophily: • Selection of similar persons as contacts • Becoming more similar to contacts

  13. Example: School Network

  14. SegregationExample: Chicago

  15. Segregation: Schelling Model (1)

  16. Segregation: Schelling Model (2)

  17. Segregation: Schelling Model (3)

  18. Segregation: Schelling Model (4)

  19. Segregation: Schelling Model (5)

  20. Example: Linear Schelling (-like) Model Vacant slot

  21. PART II: Friends and Foes

  22. Positive and Negative Relationships Negative Relationships: • “Real Life”: people you don’t like, rivals, enemies • Online: Slashdot, Epinions • Economy: competitors • Countries: enemies - + - + - + - - - +

  23. Structural Balance Unbalanced Balanced

  24. Structural Balance: Global Consequences

  25. Weak Structural Balance • In addition to triangles in Structural Balance: • Allow: triangles with 3 negative edges • Global consequences:

  26. Further Generalizations • Incomplete networks: Structural Balance iff can be extended to complete balanced network by adding signed edges • Approximate Balanced Networks: Balance property can be violated for fraction of triangles

  27. International Relations (1) USA + - + - + USRR China Pakistan - - - - India + North Vietnam

  28. International Relations (2)

  29. PART III: The Small World Phenomenon

  30. Small World and „Six Degrees of Separation“ • Small Word Phenomenon: Paths connecting two people in a social network are short(Pop Culture: „Six Degrees of Separation“) • Milgram Experiment (1960s): • Ask set of „starters“ to forward a letter to „target“ person • „starters“ are given some information, e.g. address, occupation • Rule: forward letter to person‘s you know on a first-name basis

  31. Milgram Experiment: Results

  32. Small Wold: MS Instant Messenger

  33. Modelling the Small World Phenomenon (1)

  34. Model (2): Watts-Strogatz

  35. Model (2): Watts-Strogatz contd.

  36. Decentralized Search • Watts-Strogatz model does not explain feasibility of decentralized search

  37. Modelling Decentralized Search • Idea: probability of random edge beteen nodes v and w decay with distance:~ d(v,w)q

  38. What‘s the best q for decentralized search?

  39. Decentralized Search: Explaination

  40. Generalization of Distance Decay: Rank Decay Idea: probability of random edge beteen nodes v and w decay with rank of distance:~ rank(w)p Optimal p: -1

  41. Empirical Evidence: LiveJournal Experiment

  42. Seminar Papers

  43. Papers (1): Small World Phenomenon • Jeffrey Travers, Stanley Milgram: An experimental study of the small world problem. Sociometry, 1969, 32(4): 425-443 • Jure Leskovec, Eric Horvitz: Planetary-scale views on a large instant-messaging network. WWW 2008: 915-924.

  44. Papers (2): FriendsandFoes • Jure Leskovec, Daniel Huttenlocher, Jon Kleinberg: Signed networks in social media. CHI 2010: 1361-1370. • JérômeKunegis, Andreas Lommatzsch, Christian Bauckhage: The slashdot zoo: mining a social network with negative edges. WWW 2009: 741-750.

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