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Competitive Viral Marketing Based on: S. Bharathi et al. Competitive Influence Maximization in Social Networks, WINE 2007. C. Budak et al. Limiting the.

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Presentation on theme: "Competitive Viral Marketing Based on: S. Bharathi et al. Competitive Influence Maximization in Social Networks, WINE 2007. C. Budak et al. Limiting the."— Presentation transcript:

1 Competitive Viral Marketing Based on: S. Bharathi et al. Competitive Influence Maximization in Social Networks, WINE 2007. C. Budak et al. Limiting the spread of misinformation in social networks. In WWW'11. A. Borodin et al. Threshold models for competitive influence in social networks. In Proc. 6th Workshop on Internet and Network Economic, 2010. X. He et al. Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model. In Proc. 2012 SIAM Conf. on Data Mining, pages 463–474, 2012. W. Chen et al. Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate. In SDM, pages 379–390, 2011.

2 Why Competition? To be blunt, non-competitive is not super realistic. Marketing of products, spread of ideas, innovations, political campaigns – all involve competition.

3 A Model Core – independent cascade.

4 The Model Core – independent cascade. Inacti -ve active Note: In this and subsequent models, we will use color as a metaphor for state. Many shades of activity

5 The Model Core – independent cascade.

6 The Model Core – independent cascade.

7 The Model What if competing campaigns succeeded in activating a node?

8 The Model What if competing campaigns succeeded in activating a node? Note: Ties avoided elegantly using the notion of time delay for activation, which is a continuous random variable.

9 Properties of the Model

10 Discrete Time Models Recall, classical IC/LT models are discrete time. Use of continuous time in Bharathi et al.’s competitive IC model is mainly to avoid ties in activation by rival campaigns. Discrete time will bring us back to ties and we need tie-breaking rules. E.g., competitive IC model: what if two different companies succeed in activating a node? Several options exist.

11 (Discrete Time) CIC Model

12 CIC Model (contd.) Successful attempts by blue-active in-neighbors. Successful attempts by red-active in-neighbors.

13 CIC Model (contd.)

14 CIC Model (concluded)

15 Competitive LT (CLT) Model

16 CLT Model (contd.)

17 CIC – Equiv. Live-Edge Model

18 CIC – Equivalent Live-Edge Model

19 CLT – Equivalent Live-Edge Model

20 What do we want to optimize?

21 What else do we want to optimize?

22 Influence Maximization under Competition


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