The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authoritative ” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages ” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for link-based analysis.

Consider two user populations, of which one is targeted and the other is not. Users in the targeted population follow a Markov chain on a space of n states. The untargeted population follows another Markov chain, also defined on the same set of n states. Each time a user arrives at a state, he/she is presented with information appropriate for the targeted population (an advertisement, or a recommendation) with some probability. Presenting the advertisement incurs a cost. Notice that while the revenue grows in proportion to the flow of targeted users through the state, the cost grows in proportion to the total flow (targeted and untargeted) through the state. How can we compute the best advertisement policy ? The world-wide web is a natural setting for such a problem. Internet service providers have trail information for building such Markovian user models where states correspond to pages on the web. In this paper we study the simple problem above, as well as the variants with multiple...