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Speaker : Yu- Hui Chen

Predicting YouTube Content Popularity via Facebook Data: A Network Spread Model for Optimizing Multimedia Delivery. Speaker : Yu- Hui Chen Authors : Dinuka A. Soysa , Denis Guangyin Chen, Oscar C. Au, and Amine Bermak

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Speaker : Yu- Hui Chen

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  1. Predicting YouTube Content Popularity viaFacebook Data: A Network Spread Model forOptimizing Multimedia Delivery 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)

  2. outline • Introduction • Methodology • Simulation results • Future work • 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

  9. 3.Simulation results

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

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

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

  13. 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

  14. 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

  15. 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. • Alarge network can be partitioned into multiple small networks .(ex. Hong Kong)

  16. 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.

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