Information Spread and Information Maximization in Social Networks

1. Information Spread and Information Maximization in Social Networks Xie Yiran 5.28

2. Spreading Through Networks

4. Application: viral marketing Purchase decisions are increasingly influenced by opinions of friends in Social Media How frequently do you share recommendations online? 26

5. Viral/Word-of-Mouth Marketing Idea: exploit social influence for marketing Basic assumption: word-of-mouth effect ◦ Actions, opinions, buying behaviors, innovations, etc. propagate in a social network Target users who are likely to produce word-of-mouth diffusion    ◦ Additional reach, clicks, conversions, brand awareness ◦ Target the influencers 27

6. Social networks & marketing 29

8. Identifying influencers: start-ups Klout ◦ Measure of overall influence online (mostly Twitter, now FB and LinkedIn) ◦ Score = function of true reach, amplification probability and network influence ◦ Claims score to be highly correlated to clicks, comments and retweets Peer Index ◦ Identifies/Scores authorities on the social web by topic SocialMatica ◦ Ranks 32M people by vertical/topic, claims to take into account quality of authored content Influencer50     ◦ Clients: IBM, Microsoft, SAP, Oracle and a long list of tech companies + Svnetwork, Bluecalypso, CrowdBooster, Sproutsocial, TwentyFeet, EmpireAvenue, Twitaholic , and many others … 31

11. Finding the influencers … “He’s not a ‘Super Influencer’, he’s a very naughty boy!” 32

12. Homophily or Influence? Homophily: tendency to stay together with people similar to you “Birds of a feather flock together” E.g. I’m overweight  I date overweight girls Influence: force that a person A exerts on a person B that changes the behavior/opinion of B Influence is a causal process E.g. my girlfriend gains weight  I gain weight too 36

13. Viral marketing & The Influence Maximization Problem 33   Problem statement: ◦ find a seed-set of influential people such that by targeting them we maximize the spread of viral propagations 33

14. Word-of-mouth (WoM) effect in social networks xphone is good xphone is good xphone is good xphone is good xphone is good xphone is good xphone is good Word-of-mouth (viral) marketing is believed to be a promising marketing strategy.  Increasing popularity of online social networks may enable large scale viral marketing  2

16. Diffusion/Propagation Models and the Influence Maximization (IM) Problem 4

29. Node v • fv (s) : threshold function for v • θv : threshold for v • Reward function : r(A(S)) • A(S) : final set of active nodes • Influence spread: T-1 T

32. Use greedy algorithm framework • Use Monte Carlo simulations to estimate 𝜎 𝑆

39. Influence spread 𝜎 𝑆 is submodular in both IC and LT models

40. Scalable Influence Maximization 31

41. Theory versus Practice ...the trouble about arguments is, they ain't nothing but theories, after all, and theories don't prove nothing, they only give you a place to rest on, a spell, when you are tuckered out butting around and around trying to find out something there ain't no way to find out... There's another trouble about theories: there's always a hole in them somewheres, sure, if you look close enough. - “Tom Sawyer Abroad”, Mark Twain 32

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