Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding user behavior in large-scale video-on-demand systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Characterizing social cascades in flickr
Proceedings of the first workshop on Online social networks
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
SpinThrift: saving energy in viral workloads
COMSNETS'10 Proceedings of the 2nd international conference on COMmunication systems and NETworks
SpinThrift: saving energy in viral workloads
Proceedings of the first ACM SIGCOMM workshop on Green networking
The freshman handbook: a hint for the server placement of social networks
Proceedings of the 20th international conference companion on World wide web
TailGate: handling long-tail content with a little help from friends
Proceedings of the 21st international conference on World Wide Web
Storage and performance optimization of long tail key access in a social network
Proceedings of the 3rd International Workshop on Cloud Data and Platforms
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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.