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Online Social Networks

Online Social Networks. Thomas Karagiannis Microsoft Research. How many people in the room have a profile in an Online Social Network (OSN)?. Real life…. ...and the networking community .

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Online Social Networks

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  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 Routing, Security, Data Center Networking, Management, Wireless, Router Primitives, Incentives, Measurement, P2P Multicast and Anycast, Control mechanisms, WWW, Performance analysis, Routing, TCP, Tracing and Measurement, Header Processing

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

  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 • How are groups of people connected? [Milgram 1967]

  15. Six Degrees of Separation Six hops on the average to reach the target! • 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 • 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 [Granovetter 1973] • 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

  19. Bridges and the strength of weak ties [Granovetter 1973] • 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 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 10 9 2 8 5 3 1 4 6 7

  22. Closeness centrality • 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 10 9 2 8 5 3 1 4 6 7

  23. Closeness centrality • 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):

  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): 10 9 2 8 5 3 1 4 6 7

  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 [Mislove et al, IMC-2007] • Crawled of several online social networks • Flickr: photo sharing • LiveJournal: blogging site • Orkut: social networking site • YouTube: video sharing

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

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

  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 [Adamic et al, 2005] • 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

  32. Corporate email social networks and degree distributions [Karagiannis et al, MSR-TR 2008] • 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)

  33. Measurement of Online Social Networks [Mislove et al, IMC 2007] Link symmetry • 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 Email graph • Strength defined based on volume • Removal of weak ties does not affect the global connectivity • Strong connectivity may be the result of the imposed org structure

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

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

  40. Sociability and number of friends [Chun et al, IMC-2008] Capacity cap • 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 • 82% users have less than 1% overlap between the two networks • Business network has lesser degree • 50% of users have less than 10 friends or transaction partners

  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. Viral marketing and social networks • 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

  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. Cascades in social networks • Hypothesis: Photos propagate like diseases through human contacts • Model: • k: node degree, σ0 :adoption rate • Known R0 : HIV (2-5), Measles (12-18)

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