We argue that relationships between Web pages are functions of the user's intent. We identify a class of Web tasks - information-gathering - that can be facilitated by a search engine that provides links to pages which are related to the page the user is currently viewing. We define three kinds of intentional relationships that correspond to whether the user is a) seeking sources of information, b) reading pages which provide information, or c) surfing through pages as part of an extended information-gathering process. We show that these three relationships can be productively mined using a combination of textual and link information and provide three scoring mechanisms that correspond to them: SeekRel , FactRel and SurfRel . These scoring mechanisms incorporate both textual and link information. We build a set of capacitated subnetworks - each corresponding to a particular keyword - that mirror the interconnection structure of the World Wide Web. The scores are computed by computing flows on these subnetworks. The capacities of the links are derived from the hub and authority values of the nodes they connect, following the work of Kleinberg (1998) on assigning authority to pages in hyperlinked environments. We evaluated our scoring mechanism by running experiments on four data sets taken from the Web. We present user evaluations of the relevance of the top results returned by our scoring mechanisms and compare those to the top results returned by Google's Similar Pages feature, and the Companion algorithm proposed by Dean and Henzinger (1999).
Networked information spaces contain information entities, corresponding to nodes, which are connected by associations, corresponding to links in the network. Examples of networked information spaces are: the World Wide Web, where information entities are web pages, and associations are hyperlinks: the scientific literature, where information entities are articles and associations are references to other articles. Similarity between information entities in a networked information space can be defined not only based on the content of the information entities, but also based on the connectivity established by the associations present. This paper explores the definition of similarity based on connectivity only, and proposes several algorithms for this purpose. Our metrics take advantage of the local neighborhoods of the nodes in the networked information space. Therefore, explicit availability of the networked information space is not required, as long as a query engine is available for following links and extracting the necessary local neighbourhoods for similarity estimation. Two variations of similarity estimation between two nodes are described, one based on the separate local neighbourhoods of the nodes, and another based on the joint local neighbourhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on the citation graph of computer science. The immediate application of this work is in finding papers similar to a given paper in a digital library, but they are also applicable to other networked information spaces, such as the Web. Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval.