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Web Science Course 2014 - Lecture : Social Networks - *

This lecture explores the intricate world of social networks, examining entities (individuals, organizations) and their connections (friendships, collaborations). It delves into examples ranging from real-life relationships to online platforms like Facebook and Twitter, as well as trading networks. Key topics include homophily, the Small World Phenomenon, and structural balance in relationships. The discussion aims to answer research questions about network formation, information propagation, and community detection, highlighting real-world implications across various domains such as economics, health, and communication.

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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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