Abstract

An important problem faced by users of large networks is how to discover resources of interest, such as data or people. In this paper we focus on locating people with particular interests or expertise. The usual approach is to build interest group lists from explicitly registered data. However, doing so assumes one knows what lists should be built, and who should be included in each list. We present an alternative approach, which can support a more fine grained and dynamically adaptive notion of shared interests. Our approach deduces interests from the history of electronic mail communication, using a set of heuristic graph algorithms. We demonstrate the algorithms by applying them to data collected from 15 sites for two months. Using these algorithms we were able to deduce shared interest lists for people far beyond the data collection sites. The algorithms we present are powerful, and if abused can threaten privacy. We propose guidelines that we believe should underly the ethical use of these algorithms. We discuss several possible applications that we believe do not threaten privacy, including discovering resources other than people, such as file system data.

Citations