1. Predicting Product Adoption in Large-Scale Social Networks Offensive: Hao Chen

2. General Problems: Can IM system be a good social network example? By only study one product sample, can we generalize the universe of all niche products.

3. General Problems: Only 5 citations, not much contribution to field. The paper is not well structured. – Left lots of figures and tables without detailed information. – Not giving clear definition of jargon(cascade of adoption).

4. Dataset Problems: Used a large scale data without detailed information. – Did the database change in two years? “Re-scaled for confidentiality reasons.” Is the given data sufficient?

5. Dataset Problems: In which country did they gather the data. – Country is most important factor. – Different conclusion in different countries?

6. Analyzing Influencers: What kind of marketing strategy is used in this analysis? – Direct Marketing or Social Neighborhood Marketing? Unconvincing conclusion: peer pressure is more important. – In other social network.

7. Analyzing Influencers: What is the connectivity of the premium subgraph? – Size of connected subgraph in average?

8. Analyzing Influencers: What is the standard to decide high-degree users? Dose high-degree users equal to influencers? – Unconvincing simplicity.

9. Analyzing Influencers: Registration information:

10. Analyzing Influencers: What is the value of x?

11. Analyzing Influencers:

12. Evaluating Marketing Strategies: Using some machine learning algorithms without explanation. – Why did you choose this algorithm.

13. Evaluating Marketing Strategies: How to evaluate the different methods:

14. Thank you!