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

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

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.

2.Methodology An example infection process of Independent Cascade Model

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

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

C) Fast Threshold Spread Model The Facebook social graph extracted is modelled as an undirected graph with vertices,, in as the users in the network and edges,, in as the relationship between individuals. For each interuser edge k, we evaluate the weight function: where is the average number of posts posted per week by user m and is the average number of shares plus the number of comments plus the number of likes for each of his posts. They are averaged to yield W(m) which indicates the social influence of a given user.

D) Complexity Analysis on a Small Network vs a Large Network Each user node m will be either active or inactive. The total number of activated nodes, also known as the influence spread, is denoted by NumActiveNodes. The Threshold value is chosen by simulation to be 4.0.

D) FTSM algorithm

D) Complexity Analysis on a Small Network vs a Large Network Let di be the number of neighbors of node i. is the probability that the computed W(m) in Eq. 2 is greater than Threshold in Eq. 3 。 is the probability that the node is not accessed before and activated in the past be the computation complexity

D) Complexity Analysis on a Small Network vs a Large Network Since the above equations follow a geometric progression,the sum of all the m terms can be calculated by

D) Complexity Analysis on a Small Network vs a Large Network N be the number of hops(number of iterations) It can be seen that the number of computations increase in the power law of N when N is increased. The value of P(notRepeat) is decaying as more and more nodes get activated. It is more advantageous to simulate ina small network

The highlighted circle represents a small network observation, that shows a similar spread pattern to the large network.

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

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

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

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

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)

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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