1. Online Social Networks Thomas Karagiannis Microsoft Research

2. How many people in the room have a profile in an Online Social Network (OSN)?

3. Real life…

4. ...and the networking community Network geometry and design, Inference of network properties, Multihoming and overlays, Wireless, Secure networks, Troubleshooting, Congestion control, Router design, DNS Multicast and Anycast, Control mechanisms, WWW, Performance analysis, Routing, TCP, Tracing and Measurement, Header Processing Routing, Security, Data Center Networking, Management, Wireless, Router Primitives, Incentives, Measurement, P2P

5. Social networking services Social communities – Bebo, MySpace, Facebook, etc. Content sharing – YouTube, Flickr, MSN Soapbox, etc. Corporate – LinkedIn, Plaxo, etc. Portals – MSN, Yahoo 360, etc. Recommendation engines – Last.fm, StumbleUpon, Digg, Me.dium, etc. Bookmarking/Tagging – Del.icio.us, CiteUlike, Furl, etc. Discussion groups – Blogs, forums, chat, messaging, Live QnA, etc. Mobile social networks – Vipera, Nokia “MOSH”, etc. Virtual worlds – Second life

6. Social Network Sites: History [Boyd et al., 2007]  SixDegrees.com the first recognizable OSN  Profiles and lists of friends  Combined existing features!  Failed - Nothing to do after accepting friend requests.  OSN wave after 2001  Friendster:  Technical and social difficulties with scale!  “Fakesters” diluted the community  MySpace:  Capitalized on Friendster’s problems  Bands and fans  Allowed personalization of profiles  Facebook:  Growth: Harvard-only => University-only => high schools & professionals => everyone  Introduced applications (provided APIs)

7. Social networking services Source: Bebo, Social Media – ‘getting your message across’  Shift in online communities  OSNs are organized around people  “Egocentric” networks  WEB: world composed of groups  OSNs: world composed of networks

8. What do social networks enable? Leveraging the “community” in traditional applications Content/information sharing Search Information management Recommendations Advertisements

9. Research topics of interest Identification of communities and their evolution in time Measurement and analysis of online communities Social media analysis: blogs and friendship networks Recommendation / collaborative filtering systems Rating, review, reputation, and trust systems Expertise / interest tracking Information sharing and forwarding Search strategies in social networks Viral marketing strategies Implications on network and distributed systems design System design for social networks Mobile social networks Privacy and anonymity

10. This lecture Social networks —Sociological studies & basic concepts —Small worlds, weak ties, degrees, centralities Analysis and measurements of OSNs —Structure and properties —Impact of OSNs on traditional applications and user activity —Information dissemination, viral marketing, privacy, tagging

11. Networks.. …an interconnected system …a series of points or nodes interconnected by communication paths … a collection of computers connected to each other

12. ..and networks …relations, social structure among a set of actors (i.e., individuals) …nodes (which are generally individuals or organizations) that are tied by one or more specific types of interdependency, such as values, visions, ideas, financial exchange, friendship

13. Sociological studies How are groups of people connected? —To what degree does every member of a given group know every other member? —Six degrees of separation and the small world phenomenon How many people do you know? —Ego networks Communities and interactions —Zachary’s karate club The strength of weak ties —Bridges and structural holes

14. Six Degrees of Separation Arbitrary “starting persons” were selected to forward a letter to a first-name acquaintance with the final goal of reaching an “arbitrary” target person –Target: Stockbroker in Boston, MA. –Starters: Random sample (n=100) of Boston residents Random sample (n=96) from all Nebraska residents Sample (n=100) of share-owning Nebraska residents [Milgram 1967] How are groups of people connected?

15. Six Degrees of Separation 64 / 296 reached the target Forwarding by exploiting targets’ address: 6.1 Forwarding by exploiting targets’ job: 4.6 Chains overlap as they converge on the target –Only 26 individuals in the last hop –16 copies delivered from one person alone Six hops on the average to reach the target! Incomplete chains Chances of forwarding increases with number of intermediaries

16. How many people do you know? Ego networks consist of a focal node ("ego") and the nodes to whom ego is directly connected to ("alters") plus the ties [Ithiel de Sola Pool 1978] [Freeman and Thomson 1989] [Heran 1988]

17. Communities and interactions “Friendship” network between karate club students During the study, a dispute arose and the club split in two Split was the minimum cut! [Zachary 1977]

18. Bridges and the strength of weak ties Social relationships are of varying “strength” –Duration, emotional intensity, intimacy, exchange of services (backscratching) Strength of ties reveal different social processes –Strong ties tend to form cliques [Granovetter 1973]

19. Bridges and the strength of weak ties Weak ties “bridge strongly” connected components Weak ties enable the sharing of information Weak ties are related to “structural holes” [Burt 1992] –Separation between non-redundant contacts –Efficiency of ego’s network (i.e., social capital) inversely proportional to the redundancy in the network [Granovetter 1973] Bridge

20. Centralities Centrally positioned nodes are “privileged” –Hubs where power concentrates Different viewpoints: [Freeman 1979] –Degree centrality –Closeness centrality –Betweenness centrality

21. Degree centrality Centrality according to the number of connections –Degree: Number of direct links For vertex u: For a graph G(V,E): –C = 1 a node dominates –C = 0 all nodes equal centrality 1 8 2 34 7 6 5 9 10

22. Degree centrality only measures number of connections –Nodes 2,3,4,1 are equivalent Closeness centrality refers to the closeness of a node to all other network members –Node 1 is less hops away to peripheral nodes Closeness centrality 1 8 2 34 7 6 5 9 10

23. Closeness is the mean geodesic distance (i.e., shortest path) of u to all other vertices For vertex u: –As closeness increases, an individual’s access to information, power, prestige, etc. increases. [Leavitt 1951, Coleman 1973, Burt 1982] For a graph G(V,E): Closeness centrality

24. Betweenness centrality Betweeness measures the individual’s intermediary value to all members of a network –Reflects the number of geodesics through a node –Stricter measure of centrality Number of geodesics through i: For vertex u: For a graph G(V,E): 1 8 2 34 7 6 5 9 10

25. The meaning of centralities Degree centrality: –Capacity to develop communication within a network Closeness and betweenness centrality: –Capacity to control communication in a network –Closeness less accurate Strong closeness or betweeness: –Minority of actors control communications Centralities do not account for the volume of communication –Flow betweenness

26. This lecture Social networks —Sociological studies & basic concepts —Small worlds, weak ties, degrees, centralities Analysis and measurements of OSNs —Structure and properties —Impact of OSNs on traditional applications and user activity —Information dissemination, viral marketing, privacy, tagging

27. Measurement of Online Social Networks Crawled of several online social networks – Flickr: photo sharing – LiveJournal: blogging site –Orkut: social networking site –YouTube: video sharing [Mislove et al, IMC-2007]

28. Measurement of Online Social Networks- Degree Distributions [Mislove et al, IMC 2007]

29. Measurement of Online Social Networks- Degree Distributions [Leskovec et al, WWW 2008] 180M nodes 1.3 B edges

30. Corporate email social networks and degree distributions Email exchanges form a social graph –Corporate email graphs of particular interest –Problem: What constitutes an edge? Studies: –HP Labs : 430 individuals, 6 emails as a threshold, 3 months [Adamic et al, Social Networks-2005] –Microsoft : 150K employess, varying thresholds, 3 months [Karagiannis et al, MSR-TR 2008]

31. Corporate email social networks and degree distributions  Distribution appears exponential!  Structure of the graph directly affects its searchability  Biasing towards high-degree nodes may not be as efficient in enterprise email graphs [Adamic et al, 2005]

32. Corporate email social networks and degree distributions  Distribution appears to depend on the view  In-degree vs. out-degree  Median in-degree :  50 (threshold eq to 1)  2 (threshold eq to 10)  Median out-degree :  25 (threshold eq to 1)  2 (threshold eq to 10) [Karagiannis et al, MSR-TR 2008]

33. Link symmetry Measurement of Online Social Networks [Mislove et al, IMC 2007]  Why?

34. Small world and six degrees revisited  Eccentricity is the maximum shortest path for a vertex  Radius:  Minimum eccentricity of any vertex  Diameter:  Maximum eccentricity of any vertex

35. Strength of ties Impact of strong ties –What happens to the social graph when strong/weak ties are removed? –What is a strong tie? Examine the size of the largest connected component when certain nodes are removed

36. Strength of ties Different viewpoints:

37. Strength of ties  Removal of weak ties does not affect the global connectivity  Strong connectivity may be the result of the imposed org structure Email graph Strength defined based on volume

38. Strength of ties  Giant component shrinks gradually  Overlapping communities  Bridges unlikely  Shortest path does increase  Weak ties = shortcuts AOL IM Friend Lists Strength defined as participation in triangles BuddyZoo [Shi et al, Physica-2007]

39. Sociability and number of friends Guestbook activity network 2 years worth of data How do activity graphs compare with friendship graphs? How does friendship affect sociability? [Chun et al, IMC-2008]

40. Sociability and number of friends Capacity cap [Chun et al, IMC-2008]  Node strength (sociability) increases with the number of friends up to a limit  Is 200 a capacity cap?  Authors argue that the limit could be connected to Dunbar’s number  Dunbar (1998): Limit of manageable relationships is 150 Node strength: Sum of messages across all direct edges

41. Online marketplaces and social networks Hypothesis: Transactions with friends will have higher satisfaction Overstock Auctions –Similar to eBay –Incorporates social components Friends, ratings, message boards Two networks –Personal: connecting friends –Business: based on transactions [Swamynathan et al, WOSN-2008]

42. Online marketplaces and social networks  Business network has lesser degree  50% of users have less than 10 friends or transaction partners  82% users have less than 1% overlap between the two networks

43. Online marketplaces and social networks  17K transactions studied  Only 22% are between partners connected in the social network  High success rate:  ~80% for paths up to six hops  Satisfaction does not hold at long distances in the partner network  Expected (?)

44. Viral marketing and social networks [Lerman et al,WOSN-2008] Hypothesis: Social interactions may be exploited to promote content User-submitted news stories Digg promotes stories to the front page Allows social networking: –Friends vs. fans B is A’s friend if A is watching B B is A’s fan if B is watching A

45. Viral marketing and social networks  Patterns of vote diffusion?  Predict story popularity?  In-network votes  From fans of previous voters

46. Viral marketing and social networks Data by scraping Digg:  900 newly submitted stories (2006)  200 front page stories  Time-ordered votes, user ids, etc

47.  Large number of early in-network votes is negatively correlated with the eventual popularity of the story  Intuition: If a story is truly interesting, it will be discovered by “independent” individuals Viral marketing and social networks

48. Cascades in social networks [Cha et al, WOSN-2008] How do photo bookmarks spread through social links? Crawled Flickr –2.5M users, 33M friend links, 100 days –34M bookmarks (11m distinct photos) Methodology: Did a particular bookmark spread through social links? –No: if a user bookmarks a photo and if none of his friends have previously bookmarked the photo –Yes: if a user bookmarks a photo a&er one of his friends bookmarked the photo

49. Cascades in social networks

50. Hypothesis: Photos propagate like diseases through human contacts Model: –k: node degree, σ 0 :adoption rate – – Known R 0 : HIV (2-5), Measles (12-18) Cascades in social networks

51.  Finding:  Model can describe photo propagation  Potential use:  Predicting popularity

52. Privacy in social networks [Krishnamurthy et al, WOSN-2008] Users are encouraged to share personal information –Most users unaware –External applications require users to grant access to personal info

53. Privacy in social networks  Finding:  Strong negative correlation between network size and viewable profile and friend lists  Users more sensitive about their profiles

54. Privacy in social networks  Finding:  Information leaks to third- parties as for Web

55. Privacy in social networks [Guha et al, WOSN-2008] How do you ensure the “social network” experience and keep your data private? –NOYB (None of your business) Ensuring trust –Do you trust your OSN provider? –If yes, who else can see your data? Main idea: –Profiles are composed of multiple fields –If separated, these fields do not mean much

56. Privacy in social networks

58.  Not a long term strategy!

59. Ranking and suggested candidate items Collaborative information tagging [Vojnovic et al, 2008]

60. How to suggest tags? –Goal: Learn true ranking popularity –Tags could be used for information retrieval Problem: –Users tend to imitate! Ranking and suggested candidate items

61. Summary Degrees in OSNs –Power law distributions –Exponential distributions in corporate email graphs Small world phenomenon –Present in OSNs (short paths/diameters) –Average shortest path close to 6 Weak ties –Networks robust to removal of weak ties Findings: –Capacity cap of 200 –Significant symmetry of links –Marketplaces: Social links not exploited but their usage appears promising –Digg: “In-network” votes negatively correlate with story popularity –Flickr: Photo bookmarks propagate similarly to diseases –Privacy: Concerns correlate with network size –Tagging: Users imitate biasing rankings

62. Thank you!