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Lithium- The Science of Influence

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Dr. Michael Wu on the Science of Influence

Dr. Michael Wu on the Science of Influence

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  • 1. Let’s do a live experiment on collaboration!Michael Wu, PhD (mich8elwu)Principal Scientist of AnalyticsJune 19th, 2011
  • 2. Enterprise 2.0 Boston Collaborative note-taking experiment: can we collectively tweet, RT, mention each other to produce a comprehensive set of notes for this talk #e2exp @mich8elwu #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 2
  • 3. Enterprise 2.0 Boston  SNA basics  influencer identification  internal collaboration  tools & analysis #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 3
  • 4. what is a social network?▪ social network = • collection of entities + relationship among them▪ entities = people • SNA: nodes, vertices▪ relationship = • friendship (Facebook) • colleagues (LinkedIn) • kinship, communication, etc. • SNA: edges, connections Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 4
  • 5. what is a social graph?▪ social graph = • a diagram consist of nodes + edges that represents the social network▪ key: 1 social network can have many social graph▪ my social network = • my friends + my colleagues + my relatives etc. Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 5
  • 6. a hypothetical example▪ I have 7 friends Joe Doug • colleagues @ Lithium Joe + Phil who are also colleagues • @ UC Berkeley Jack + Ryan Phil me Adam • @ Los Alamos Nat’l Lab Don + Ryan • Ryan & I overlap @ 2 jobs • we both worked for Jack + Don • but Jack + Don are not colleagues Jack Don▪ LinkedIn social graph • relationship = coworkers Ryan Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 6
  • 7. a hypothetical example▪ my drinking buddies Joe Doug • Doug, Adam + Ryan • Doug + Ryan don’t get alone, so they never go out together. Phil me Adam • Phil + Jack are drinking buddies too, but I never gone drinking with either of them because they are the big bosses. Jack Don▪ beer buddy graph • relationship = drink beer together Ryan Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 7
  • 8. a hypothetical example▪ I love badminton Joe Doug • Joe @ Lithium Jack @ UC Berkeley Don @ Los Alamos • Ryan also plays, Phil me and has play with Phil + Doug. Adam • But they are pros and play each other in tournaments, so we’ve never played them▪ badminton pal graph Jack Don • relationship = have played badminton with each other Ryan Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 8
  • 9. a hypothetical example▪ I just created 3 social graph Joe Doug from my social network▪ I can also create another: the Facebook social Phil me Adam graph▪ by specifying what relationship the edges Jack Don represent, we can get very different graphs Ryan Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 9
  • 10. what is a social network analysis (SNA)?▪ SNA = 1 4 1 3 1 1 • construction of social graphs 4 that contains the relevant 1 3 4 1 relationship 2 • the analysis of social graphs 3 by computing network metrics 5 3 7 on nodes (and edges too) 2 • Example: degree centrality 4 2 3 4 3 2 • interpreting the network 3 metrics to gaining insights + 1 1 intelligence about the social 2 2 2 3 5 network Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 10
  • 11. reading a social graph▪ most important thing when reading a social graph is to find out what relationships are being represented by the edges▪ do not try to make any inference or conclusion based on a graph about anything that is not explicitly represented by the edges Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 11
  • 12. Enterprise 2.0 Boston  SNA basics  influencer identification  internal collaboration  tools & analysis #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 12
  • 13. “Despite the wealth of data generatedon social media, no one has data onwho actually influenced who ”We need a model! Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 13
  • 14. a model for influence propagationinfluencer Domain Credibility: the influencers expertise in a specific domain of knowledge High Bandwidth: the influencers ability to transmit his expert knowledge through a social media channel Content Relevance: how closely the targets information needs coincide with the influencers expertise Timing: the ability of the influencer to deliver his expert knowledge to the target at the time when the target needed it Channel Alignment: the amount of channel overlap between the target and the influencer Target Confidence: how much the target trusts the influencer target: with respect to his information needsinfluencee Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 14
  • 15. the importance of relevance and timingfriendshiprelevant relationship FanGirl WizKidw/in 1 month1 month ago3 month ago6 month ago PopGuy Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 15
  • 16. constructing an unweighted influence graph adjacency matrix representation b degree d a b c d e f g h i j k centrality c a 0 1 1 1 1 0 0 1 0 1 0 sum 6 b 1 0 0 0 0 1 0 0 1 0 0 3 a f c 1 0 0 0 1 0 0 0 0 0 0 2e d 1 0 0 0 0 1 0 0 0 0 0 2 e 1 0 1 0 0 0 1 0 0 0 0 3 j f 0 1 0 1 0 0 1 1 0 1 1 6g g 0 0 0 0 1 1 0 1 0 0 1 4 h h 1 0 0 0 0 1 1 0 1 0 1 5 i i 0 1 0 0 0 0 0 1 0 1 1 4 j 1 0 0 0 0 1 0 0 1 0 0 3 k k 0 0 0 0 0 1 1 1 1 0 0 4 Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 16
  • 17. eigenvector centrality & Google’s PageRank▪ how does Google find the 2 2 2 2 2 most authoritative web 2 2 2 pages on the WWW? 2 2 2▪ WWW = web pages 2 2 2 22 2 + hyperlinks between them 2 2 2 2 22 2 2 2 2▪ PageRank  authoritative 2 2 web pages 2 2 2 Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 17
  • 18. eigenvector centrality ~ Google’s PageRank▪ mathematically, this is the 2 2 2 2 2 same problem as finding 2 2 2 influential users in the 2 2 2 community 2 2 2 22▪ web pages  users 2 2 2 2 2 22▪ hyperlink  2 2 2 2 communication + 2 2 2 2 interactions 2 Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 18
  • 19. eigenvector centrality ~ Google’s PageRank▪ # = connections • Only ≥ 10 are labeled 12 29▪ who is most 11 authoritative? 12 18 10 32 11 Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 19
  • 20. betweenness centrality▪ # = connections • Only ≥ 10 are labeled 12 29▪ who is most 11 authoritative?▪ connector, bridge, 12 18 boundary spanner, gate keeper, innovator, 10 32 hidden influencers, … 11 Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 20
  • 21. ▪ a real social graph of a community w/ 4 sub-communities ▪ they are all connected by a single network bridge (with only 10 connections)Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 21
  • 22. Enterprise 2.0 Boston  SNA basics  influencer identification  internal collaboration  tools & analysis #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 22
  • 23. tug o’ war▪ relevant relationship ▪ data you can get • collaborated on some project • communication: emails, IMs, phone • produced some products/services calls, sms messages, etc. together • meetings: calendar data • co-authored, co-created, or co- • content usage: downloads, edits, or designed something sharing of content by someone else Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 23
  • 24. a hypothetical example▪ 1 eMail exchange/day • 5 emails w/ 1 replies • 5 emails w/ >5 replies • 5 emails w/ >10 replies Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 24
  • 25. a hypothetical example▪ 1 eMail exchange/day • 5 emails w/ 1 replies • 5 emails w/ >5 replies • 5 emails w/ >10 replies▪ 1 IM session/week • >5 sessions/week • >10 sessions/week Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 25
  • 26. a hypothetical example▪ 1 eMail exchange/day • 5 emails w/ 1 replies • 5 emails w/ >5 replies • 5 emails w/ >10 replies▪ 1 IM session/week • >5 sessions/week • >10 sessions/week▪ 1 meeting/month • >3 meetings/month • >5 meetings/month Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 26
  • 27. a hypothetical example▪ 1 eMail exchange/day CEO marketing database • 5 emails w/ 1 replies PR guy • 5 emails w/ >5 replies • 5 emails w/ >10 replies▪ 1 IM session/week Sales + + = PM • >5 sessions/week Rep1 = collaborated • >10 sessions/week▪ 1 meeting/month • >3 meetings/month Java Sales developer • >5 meetings/month Rep2 accounts/finance Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 27
  • 28. a hypothetical example▪ Collaboration means different CEO marketing database things for different roles PR guy • For product team: lots of IMs and long email threads • For executives & managers: lot of meetings together Sales PM • Email (or any single data source) Rep1 is usually not a good indicator of collaboration. People could email simply b/c they are friends Java 5 emails w/ >5 replies Sales developer >10 IM sessions/week Rep2 >5 meetings/month accounts/finance Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 28
  • 29. in summary▪ you must define what 1 4 1 3 1 1 collaboration means in 4 1 terms of the data you 3 4 1 can get before you can 2 quantify collaboration 3 5 3 7▪ then we can construct 2 2 4 the collaboration graph 3 3 4 2▪ compute network metrics 1 3 & quantify collaboration 1 2 5 2 2 3 Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 29
  • 30. Enterprise 2.0 Boston  SNA basics  influencer identification  internal collaboration  tools & analysis #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 30
  • 31. SNA tools and libraries ▪ Open source SNA tools ▪ Open source SNA libraries ▪ C++ scale / power • moderate scale: ~millions of nodesease of use • many algorithms ▪ Java Pajek • very large scale 10s−100M nodes • few metrics Enterprise 2.0 Boston #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 31
  • 32. Enterprise 2.0 Boston Analysis of the live experiment #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 32
  • 33. Enterprise 2.0 Boston Thank you Q&A + discussion #e2exp | tw: mich8elwu linkedin.com/in/MichaelWuPhD 33