Interest-aware information dissemination in small-world communities

Information dissemination is a fundamental and frequently occurring problem in large, dynamic, distributed systems. We propose a novel approach to this problem, interest-aware information dissemination, that takes advantage of small-world usage patterns in data-sharing communities. These small-world characteristics suggest that users naturally form groups of common interest. We propose algorithms for identifying these groups dynamically, without a need for explicit classification of topics or declaration of user interests. These algorithms use information about the data consumed by users to identify, via online computation, groups with similar interests. As a proof of concept, we apply this methodology to the problem of locating files in large user communities. Using real-world traces from a scientific community and from a peer-to-peer system, we show that proactive information dissemination within groups of common interest can reduce the search load by up to 70%. In addition, this approach naturally supports the efficient discovery of collections of files, a requirement specific to scientific data analysis tasks. We hypothesize that our algorithms can find numerous other uses in distributed systems, such as reputation management.