On managing social data for enabling socially-aware applications and services
Proceedings of the 3rd Workshop on Social Network Systems
GoDisco: selective gossip based dissemination of information in social community based overlays
ICDCN'11 Proceedings of the 12th international conference on Distributed computing and networking
K-path centrality: a new centrality measure in social networks
Proceedings of the 4th Workshop on Social Network Systems
Semantic methods for p2p query routing
MATES'05 Proceedings of the Third German conference on Multiagent System Technologies
Decentralized information dissemination in multidimensional semantic social overlays
ICDCN'12 Proceedings of the 13th international conference on Distributed Computing and Networking
Pervasive and Mobile Computing
SMBSRP: a search mechanism based on interest similarity, query relevance and distance prediction
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Web caches, content distribution networks, peer-to-peer file-sharing networks, distributed file systems, and data grids all have in common that they involve a community of users who use shared data. In each case, overall system performance can be improved significantly by first identifying and then exploiting the structure of community's data access patterns. We propose a novel perspective for analyzing data access workloads that considers the implicit relationships that form among users based on the data they access. We propose a new structure—the interest-sharing graph—that captures common user interests in data and justify its utility with studies on four data-sharing systems: a high-energy physics collaboration, the Web, the Kazaa peer-to-peer network, and a BitTorrent file-sharing community. We find small-world patterns in the interest-sharing graphs of all four communities. We investigate analytically and experimentally some of the potential causes that lead to this pattern and conclude that user preferences play a major role. The significance of small-world patterns is twofold: it provides a rigorous support to intuition and it suggests the potential to exploit these naturally emerging patterns. As a proof of concept, we design and evaluate an information dissemination system that exploits the small-world interest-sharing graphs by building an interest-aware network overlay. We show that this approach leads to improved information dissemination performance.