A Latent Social Approach to YouTube Popularity Prediction

Current works on Information Centric Networking assume the spectrum of caching strategies under the Least Recently/Frequently Used (LRFU) scheme as the de-facto standard, due to the ease of implementation and easier analysis of such strategies. In this paper we predict the popularity distribution of YouTube videos within a campus network. We explore two broad approaches in predicting the popularity of videos in the network: consensus approaches based on aggregate behavior in the network, and social approaches based on the information diffusion over an implicit network. We measure the performance of our approaches under a simple caching framework by picking the k most popular videos according to our predicted distribution and calculating the hit rate on the cache. We develop our approach by first incorporating video inter-arrival time (based on the power-law distribution governing the transmission time between two receivers of the same message in scale-free networks) to the baseline (LRFU), then combining with an information diffusion model over the inferred latent social graph that governs diffusion of videos in the network. We apply techniques from latent social network inference to learn the sharing probabilities between users in the network and apply a virus propagation model borrowed from mathematical epidemiology to estimate the number of times a video will be accessed in the future. Our approach gives rise to a 14% hit rate improvement over the baseline.