Abstract
Web 2.0 sites have made networked sharing of user generated content increasingly popular. Serving rich-media content with strict delivery constraints requires a distribution infrastructure. Traditional caching and distribution algorithms are optimised for globally popular content and will not be efficient for user generated content that often show a heavy-tailed popularity distribution. New algorithms are needed.
This paper shows that information encoded in social network structure can be used to predict access patterns which may be partly driven by viral information dissemination, termed as a social cascade. Specifically, knowledge about the number and location of friends of previous users is used to generate hints that enable placing replicas of the content closer to future accesses.