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Competition in VM – Completing the Circle. Previous work in Competitive VM Mainly follower’s perspective: given state (say of seed selection) of previous.

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Presentation on theme: "Competition in VM – Completing the Circle. Previous work in Competitive VM Mainly follower’s perspective: given state (say of seed selection) of previous."— Presentation transcript:

1 Competition in VM – Completing the Circle

2 Previous work in Competitive VM Mainly follower’s perspective: given state (say of seed selection) of previous companies (agents/players): – what’s the best strategy for the “follower” to maximize its spread in the face of the competition? – What’s the best strategy for the follower to maximize the blocked influence of the opponent? Most competitive VM algorithms not scalable or assume unfettered access to the n/w for all players. – How realistic is that?

3 But … Campaign runners don’t necessarily have unfettered access to the network! There is an owner of the network. Campaigns need owner’s permission. May need to pay the owner.

4 A New Business Model – Introducing … Network owner Provides VM service. How should the host select/allocate seeds? I need 100 seeds I need 250 seeds Competition starts after host selects/allocates seeds.

5 Business Model (contd.)

6 Why is fairness important? Imaginary scenario: SONY NEX-VG30H 50 seeds Spread 1000 JVC HD Everio GZ-VX seeds Spread 240 For comparable products, if the b4b is substantially different, dissatisfied company(ies) may take their VM business elsewhere!

7 Propagation Model Which model should we use? (C)LT – more natural for product adoption. Unfortunately, previous CLT proposals have some disadvantages. – The CLT discussed last class (abstracted from Chen et al. and Borodin et al.) – not submodular. – Spells computational difficulties. Let’s review Borodin et al.’s model next.

8 WPCLT Model inactive influenc ed active Should I buy a camcorder? Which CC should I buy? I’ve chosen my color! Seeds influence out-neighbors (followers). Active nodes influence followers. Once influenced, relative weights of different (campaign) influences induce a random decision making trial.

9 WPCLT Model Core – LT Model.

10 WPCLT Analysis u 0.5 v 0.5 w 1.0 x

11 WPCLT Analysis u 0.5 v 0.5 w 1.0 x Red seed happens to help blue’s cause!

12 Introducing the K-LT Model

13 Proof of both properties

14 Fine, what do we want to optimize?

15 Optimal Seed Selection

16 Fair Seed Allocation

17 Calculating Spread/Marginal gain Adjusted Marginal Gain u v w

18 How hard is FSA?

19 Illustrating the Reduction

20 FSA remains NP-complete but weakly so. It resembles PARTITION in this case. Can be solved in pseudo-poly time using dynamic programming. Efficient heuristic for general case: Needy Greedy!

21 Needy Greedy Algorithm

22 Experimental Evaluation & Conclusions EE: See paper. Role of host. Fair seed allocation. Complexity (strongly NP-complete) and efficient heuristic solution. Questions: What if the influence weights and node thresholds depend on the company? Other sort orders for seeds in Needy Greedy? What if the products significantly differ in popularity: e.g., fairness achievable b/w Nokia and Galaxy is limited. How do we handle this? Adaptive seed selection/allocation (game theory)? Sometimes propagation phenomenon may proceed like an “S-curve” – need “critical mass” of seeds to reach tipoff point. Blind pursuit of FSA here may be problematic.


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