ECS 289 / MAE 298, Lecture 16 May 22, Diffusion, Cascades and Influence, Part II

Transcription

1 ECS 289 / MAE 298, Lecture 16 May 22, 2014 Diffusion, Cascades and Influence, Part II

2 Announcements Homeworks HW3 and HW3b now due Tues May 27 HW3a (project progress report) due Fri May 30 NetSci 2014 attendance and volunteering Visiting Scholar, Trivik Verma, will coordinate. Expect an next week with final details. Tasks: Pass out name tags (starting at 8:15am); Pass around microphone for audience questions; etc Mathematics and Industry Seminar today, 5:10pm Project presentations

3 Mathematics and Industry Seminar today, 5:10pm Two data scientists from Yelp are speaking on their work on A/B testing and the multi-armed bandit problem; Scott Clark and Ben Goldenberg The talk is at 5:10 pm in MSB 2112 Optimally Learning for Fun and Profit Running experiments over user traffic to test feature improvements has become common over the last decade. Segmenting your traffic and showing different code paths or parameters to different buckets of users, A/B testing, allows one to iteratively improve features with statistical confidence. This problem can be aided with the use of multi-armed bandits, which optimally trade off exploration (gaining new knowledge about the system) and exploitation (gain received from the current knowledge of the system) to efficiently control these experiments. When used in experiments involving parameter search this allows for quickly determining which values are viable in the parameter space (exploration) and funneling more traffic towards them (exploitation). This method can be extended by applying optimal experimental design, using Bayesian global optimization, to suggest optimal new values to test as old ones are deemed non-viable, using information about the space gained from the running experiment. In this talk I will give a short overview of the multi-armed bandit problem and show how we are using optimal experimental design to extend the traditional multi-armed bandit approach to A/B testing and targeting.

4 13 Class projects 1. Optimal Location For a New Business Harika Sabella, Mohammad Adil, Sugeerth Murugesan 2. Contagion in Social Resistace Networks Haochen Wu, Felipe Aviles Lucero, Jaime Jackson, Aleksander Zujev, Elizabeth Zarrindast 3. Community Detection and Resilience G. Badeau, W. Cuello, R. Starr 4. Open-Source Collaboration and Following Grace Benefield, Casey Casalnuovo 5. The Effect of Signed Network Topology on Binary Neurons Tom Chartrand, Alec Boyd 6. Who Triggers a Weibo Event? Xiaotao Feng, Jiahui Guan 7. Hierarchical Structure of Wikipedia Brian Weston, Jay Gokhale, Ali Emara 8. Characterizing fmri-based functional networks during attention-demanding tasks at rest Ben Kubit, Saeedeh Komijani 9. Maternal/fetal effects of Bis(2-ethylhexyl) phthalate on hormone regulation during gestation in Zinc deficient rats: a network analysis approach Carlos Ruvalcaba, Trevor Ramsay, and Heidi Kucera

5 13 Class projects, cont 10. California Winegrower Social Networks: Environmental Certifications and Practices Michael Levy and Ryan Parker 11. Opinion Dynamics with reluctant agents Hoi-To Wai, Christopher Patton 12. Extraction and Reasoning about Roles in Power Networks Andrew Smith 13. Yelp Data challenge - Communities for A Better Yelp Edmund Yan, Leyuan Wang Volunteered to speak Thurs May mins total for each presentation (including Q&A) 5-6 presentations on Thurs May 29 (90 min slot) 7-8 presentations on Thurs June 12 (120 min slot)

6 Diffusion and cascades in networks (Nodes in one of two states) Viruses (human and computer) contact processes epidemic thresholds Adoption of new technologies Winner take all Benefit of first to market Benefit of second to market Political or social beliefs and societal norms A long history of study, now trying to add impact of underlying network structure.

7 Diffusion, Cascade behaviors, and influential nodes Part I: Ensemble models (last time) Generating functions for giant components

8 Generating functions Start from a simple probability density function, P k Can manipulate P k to build generating functions (G.F.) for more complicated distributions Edge following G.F. Component at end of random edge G.F. Component for randomly selected node G.F. Take derivatives of G.F. to calculate the moments of those distributions s, expected size of component for randomly chosen node (first moment of that G.F.) An algebraic expression. First instance that s is emergence of a giant component.

9 Generating function approach to adoption of new behavior: Watts PNAS (2002) All nodes, except one, start in inactive state, { 1} Fractional threshold model (Φ i ). Node activated once a fraction of it s neighbors Φ i are active. A vulnerable node is one that needs only a single neighbor to be active before it flips (i.e., Φ i 1/k). Use generating functions to calculate the expected size of clusters of vulnerable nodes. A Global cascade corresponds to a giant component Results Heterogeneity in thresholds (Φ i ) enhances global cascades. Heterogeneity of degree (P k ) suppresses global cascades.

10 Diffusion, Cascade behaviors, and influential nodes Part I: Ensemble models (last time) Master equation approach: Pastor-Satorras and Vespignani Contact process, epidemic spreading Probability of becoming activated is proportional to the number of active neighbors. Results Heterogeneity of degree (P k ) enhances global spreading. For PLRG with 2 < γ < 3 the epidemic threshold λ c 0.

11 SIS disease dynamics ρ k (t) is density of infected nodes of degree k at time t. (Hence [1 ρ k (t)] is probability a node of degree k is NOT infected.) λ = β/γ, the effective spreading rate. Set γ = 1. (Recall β is infection rate, γ is recovery.) The time evolution (a master equation ): dρ k (t) dt = ρ k (t) + λk [1 ρ k (t)] Θ(ρ(t)) First term: nodes recover with unit rate (γ = 1) Second term: Infection rate λ, times number of neighbors k, times prob node of degree k is healthy, times prob of being connected to an infected node Θ(ρ(t)).

12 Steady state of master eqn, dρ k dt = 0 implies: ρ k = λ k Θ 1 + λ Θ Inserting into expression for Θ: Θ = 1 k k k p k λ k Θ 1 + λ Θ (Note Θ = 0 always satisfies, but is quite dull!... ρ k = 0)

13 Searching for more solutions to last equation, in interval 0 < Θ 1 A 1 B 1 y 2 (Θ) y 2 (Θ) Slope < 1 1 Θ Θ 1 Θ If the slope of Θ > 1 at the origin, there will be a non-trivial solution in the interval 0 < Θ 1.

14 Searching for more solutions to last equation, in interval 0 < Θ 1 Taking derivative w.r.t. Θ of both sides of last equation: d dθ [ 1 k p k k solving this: 1 k k ] λ k Θ 1 + λ Θ Θ=0 k p k λ c k = k λ c = k k 2 k 2 = 1, at λ = λ c k λ c = 1 If k 2 but k finite, then λ c 0. Unlike a threshold model, they find node heterogeniety greatly enhances onset of global cascade

15 Diffusion, Cascade behaviors, and influential nodes Part II: Contact processes with individual node preferences Long history of empirical / qualitative study in the social sciences (Peyton Young, Granovetter, Martin Nowak...; diffusion of innovation; societal norms) Recent theorems: network coordination games (bigger payout if connected nodes in the same state) (Kleinberg, Kempe, Tardos, Dodds, Watts, Domingos) Finding the influential set of nodes, or the k most influential Often NP-hard and not amenable to approximation algorithms Key distinction: Thresholds of activation (leads to unpredictable behaviors) Diminishing returns (submodular functions nicer)

16 Part II. Network Coordination Games The most basic model: Reviewed in Kleinberg Cascading Behavior in Networks: Algorithmic and Economic Issues, Chap 24 of Algorithmic Game Theory, (Cambridge University Press, 2007). Again each node in one of two states, say { 1, +1}. Play a game with each connected neighbor independently. Total payout is sum over all games. Assume neighbor(s) of j in fixed state while j updates. Positive payout if connected nodes i and j adopt the same state. No payout if they differ. And -1 can have different payout that +1 coordinated behavior. Payout matrix: q 0 0 (1-q)

17 How each node operates Again assume all other nodes fixed while node j updates. It has k A j nodes in state 1, and kb j nodes in state +1. If it chooses state 1, payout of qk A j. If it chooses state +1, payout of (1 q)k B j. Chooses 1 if qk A j > (1 q)kb j. Substitute in k j = k A j + kb j and rearrange: Criteria: choose 1 if k B j < qk j and +1 if k B j > qk j. A threshold model! Adopt +1 if a fraction q of your neighbors have state +1.

18 Contagion threshold and cascades Start all nodes in 1. And all nodes update synchronously at discrete time steps. Key question: When is there a small set of nodes S, that when set to +1 convert all (or almost all) of the population? A set S is contagious if every other node is converted by S. Easier for S to be contagious if the threshold q is small. Define the contagion threshold of a graph G to be the maximum q for which there exists a finite contagous set. (Like with generating functions, here no notion of how long it takes for the full network to be activated. Just a final steadystate answer.)

19 Progressive vs. non-progressive processes The model thus far is non-progressive: nodes can flip from 1 to +1 and back to 1. This makes the situation less stable. Consider a line of all 1 at the start with a single +1 in the center, and q = 1/2. At next time steps neighbors of the +1 flip, but the +1 switches back to 1! And the whole system ends up blinking. Progressive: Once you flip, always stay in that state. The line above now all flips to +1 in a wavefront moving right and left-wards.

20 Theorem: The Contagion Threshold for any Graph is at most 1/2. (Recall the contagion threshold is the maximum value of q for which a finite contagious set exists.) Independent of progressive vs non-progressive. A behavior cant spread very far if it requires a strict majority of your friends to convince you to adopt it. This means if q > 1/2 on any graph, it cannot support a cascade and the full graph will not be activated. This is for any graph: uniform degree, power law, etc.

21 Extending this simple model So far all nodes have same fractional threshold q, and all neighbors contribute equally in calculation of fraction. The General Linear Threshold Model Directed graphs (not reciprocal influence necessarily). Each node has a threshold chosen uniformly at random between [0, 1]. Each neighbor exerts a non-negative weight. The only constraint is that sum over all the weights is less than or equal to 1. Note we now have diversity of influence (e.g., spouse/relative can exert stronger weight than coworker/friend).

22 Finding the influential nodes Motivation Viral marketing use word-of-mouth effects to sell product with minimal advertising cost. Design of search tools to track news, blogs, and other forms of on-line discussion about current events Finding the influential nodes: formally The minimum set S V that will lead to the whole network being activated. The optimal set of a specified size k = S that will lead to largest portion of the network being activated.

23 Due to thresholds/ critical mass In general NP-hard to find optimal set S. NP-hard to even find a approximate optimal set (optimal to within factor η 1 ɛ where n is network size and ɛ > 0.) ( inapproximability ) Due to thresholds (esp if each node can have its own) might have a tiny activated final set of nodes but it jumps abruptly if just a few more nodes or, moreover, the right nodes activated. Kleinberg calls this abrupt response the Knife edge property

24 Diminishing returns (No longer a threshold, but a concave function) Each additional friend who adopts the new behavior enhances your chance of adopting the new behevaior, but with less influence for each additional friend (from Leskovec talk)

25 Diminishing returns (Submodular / concave function) The benefit of adding elements decreases as the set to which they are being added grows. So no longer get to have more influence from family or other special nodes. (Instead its the first nodes exert more influence.) Since no longer have special nodes easy to build up optimal set S of k nodes. Hill climbing add one at the time nodes to the set S that cause maximum impact.

26 Hill climbing (from Leskovec talk)

27 Submodular and hill climbing more formally: (from Leskovec talk)

28 Empirical observations (from Leskovec talk)

29 (from Leskovec talk)

30 Joining Livejournal: on online bulletin board network Probability of joining a community when k friends are already members probability k Diminishing returns only sets in once k > 3. Network effect not illustrated by curve: If the k friends are highly clustered, the new user is more likely to join.

31 (from Leskovec talk)

32 (from Leskovec talk)

33 (from Leskovec talk)

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37 (from Leskovec talk)

38 For a wealth of additional information see Leskovec talk: leskovec dcbn/ The role product category plays (books, dvds, videos, anime dvds) Predicting recommendation success with linear models. How do people actually get recommendations Amazon recommendation of similar purchases Personalized rec based on previous purchases/likes 68% of people consult friends and family before purchasing home electronics [Burke 2003]. (i.e. More influenced by friends than strangers.) 94% of users make recommendations w/o having received one (they are the seed nodes)

39 Another interesting recent piece Challenging the Influentials Hypothesis, Duncan J. Watts, Measuring Word of Mouth, Volume 3, Aug 2007.

40 Summary Important distinctions for cascade processes Contagion (e.g. a virus) versus social behaviors. Threshold models / critical mass (abrupt changes as set S increased) Diminishing returns (submodular / concave) KKT03, KKT05: If individual function for all nodes exhibit diminishing returns, the resulting influence function for the graph will be submodular ( local to global ). Can approximate such sets (hill climbing)

41 Other interesting, related models on networks Voter models Synchronization (related to the spectral properties of A the adjacency matrix). Finally: Part III: Markov chains and mixing times Montanari and Saberi, The Spread of Innovations in Social Networks, PNAS 2010 Unlike any of the above models (which tell us about equilibrium sizes of activated populations), Markov Chain and mixing times tell us about the time it takes for innovations to be adopted! sluggish rapid fire spread