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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 Slide 2 outline 1. Introduction 2. Methodology 3. Simulation results 4. Future work 5. Conclusion Slide 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. Slide 4 2.Methodology An example infection process of Independent Cascade Model Slide 5 A) Facebook Data Mining Experimental setup: Requesting, downloading and analyzing JSON objects from Facebook Slide 6 B) YouTube Video Statistics Mining The YouTube statistics provided by YouTube API Slide 7 C) Fast Threshold Spread Model G=(V,E) W(m)=0.5A1(m)+0.5A2(m) Slide 8 D) Complexity Analysis on a Small Network vs a Large Network Slide 9 Slide 10 Slide 11 3.Simulation results Slide 12 A) Determining Global Threshold Effect on NumActiveNodes by changing the Threshold Slide 13 B) Power Law behavior of the Facebook Dataset Plot of Node Degree vs Number of Nodes in linear scale Slide 14 B) Power Law behavior of the Facebook Dataset Plot of Node Degree vs Number of Nodes in log scale Slide 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 predictors spread count Slide 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 Slide 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 networks. A large network can be partitioned into multiple small networks.(ex. Hong Kong) Slide 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.