1. Speaker : Yu-Hui Chen Authors : Dinuka A. Soysa, Denis Guangyin Chen, Oscar C. Au, and Amine Bermak From : 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Predicting YouTube Content Popularity via Facebook Data: A Network Spread Model for Optimizing Multimedia Delivery

2. outline 1. Introduction 2. Methodology 3. Simulation results 4. Future work 5. Conclusion

3. 1.Introduction Through websites such as Facebook and YouTube to share multimedia content, the limited network resources, access to large amounts of multimedia data is a major challenge. This paper proposes a Fast Threshold Spread Model (FTSM) to predict the future access pattern of multi-media content based on the social information of its past viewers.

4. 2.Methodology An example infection process of Independent Cascade Model

5. A) Facebook Data Mining Experimental setup: Requesting, downloading and analyzing JSON objects from Facebook

6. B) YouTube Video Statistics Mining The YouTube statistics provided by YouTube API

7. C) Fast Threshold Spread Model G=(V,E) W(m)=0.5A1(m)+0.5A2(m)

8. D) Complexity Analysis on a Small Network vs a Large Network

11. 3.Simulation results

12. A) Determining Global Threshold Effect on NumActiveNodes by changing the Threshold

13. B) Power Law behavior of the Facebook Dataset Plot of Node Degree vs Number of Nodes in linear scale

14. B) Power Law behavior of the Facebook Dataset Plot of Node Degree vs Number of Nodes in log scale

15. C) Correlation between Facebook social sharing and YouTube Global hit-count Scatter plot of top 10 viral videos’ Global YouTube hit count vs FTSM predictor’s spread count

16. D) Transient spread simulation compared with YouTube data Normalized view count for FTSM simulation (in red) and YouTube data (in blue) for top 9 viral videos in the Facebook Dataset

17. 4.Future work FTSM for a large network of a few million nodes results in very long execution time. This paper is able to show that a small network’s. A large network can be partitioned into multiple small networks.(ex. Hong Kong)

18. 5.Conclusion The Fast Threshold Spread Model (FTSM) was used to perform fast prediction of multi-media content propagation based on the social information of its past viewers. This can be a solution to the cache management challenges when prioritizing.