Information Spread and Information Maximization in Social Networks Xie Yiran 5.28

Spreading Through Networks

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

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

Social networks & marketing 29

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

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

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

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

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 xphone is good Word-of-mouth (WoM) effect in social networks xphone is good xphone is good

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

Node v –f v (s) : threshold function for v –θ v : threshold for v Reward function : r(A(S)) –A(S) : final set of active nodes –Influence spread: T-1T

Use greedy algorithm framework Use Monte Carlo simulations to estimate

Influence spread   is submodular in both IC and LT models

Scalable Influence Maximization 31

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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Learning Influence Models 11

Where do the numbers come from? 12

Learning influence models Where do influence probabilities come from? ◦◦◦◦◦◦◦◦ Real world social networks don ’ t have probabilities! Can we learn the probabilities from action logs? Sometimes we don ’ t even know the social network Can we learn the social network, too? Does influence probability change over time? ◦ Yes! How can we take time into account? ◦ Can we predict the time at which user is most likely to perform an action? 13

Where do the weights come from? Influence Maximization – Gen 0: academic collaboration networks (real) with weights assigned arbitrarily using some models: ◦ Trivalency: weights chosen uniformly at random from {0.1, 0.01, 0.001}

P1. Social network not given Observe activation times, assume probability of a successful activation decays (e.g., exponentially) with time 17 Actual networkLearned network,,,,,,,, [Gomez-Rodriguez, Leskovec, & Krause KDD 2010]

P2. Social network given 20 Several models of influence probability ◦ in the context of General Threshold model + time ◦ consistent with IC and LT models With or without explicit attribution Models able to predict whether a user will perform an action or not: predict the time at which she will perform it Introduce metrics of user and action influenceability ◦ high values  genuine influence Develop efficient algorithms to learn the parameters of the models; minimize the number of scans over the propagation log Incrementally property [[Goyal, Bonchi, and L. WSDM2010 ]

Comparison of Static, CT and DT models Time-conscious models better than the static model ◦ CT and DT models perform equally well Static and DT models are far more efficient compared to CT models because of their incremental nature 23

Predicting Time – Distribution of Error Operating Point is chosen corresponding to ◦ TPR: 82.5%, FPR: 17.5%. Most of the time, error in the prediction is very small 24