Information dissemination is a fundamental and frequently occuring 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

more »

... laration 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.