Identifying nodes of information that are highly related has many applications in any information systems, and in particular in hypertext systems. In this paper we present a technique to identify “natural” clusters in a hypertext. A natural cluster is a cluster that is not arbitrary, but depends only on intrinsic properties of the hypertext. In our case, the property we will use to identify the clusters is the number of independent paths between nodes. Using the graph theoretic definition of k-edge-components we present an aggregation technique to cluster the nodes. We then use this techniques to cluster three medium sized hypertexts that were developed by different authors for different users, using different methodologies. We also show how to use clustering to improve data display, browsing and retrieval.

Hypertext users often suffer from the “lost in hyperspace” problem: disorientation from too many jumps while traversing a complex network. One solution to this problem is improved authoring to create more comprehensible structures. This paper proposes several authoring tools, based on hypertext structure analysis. In many hypertext systems authors are encouraged to create hierarchical structures, but when writing, the hierarchy is lost because of the inclusion of cross-reference links. The first part of this paper looks at ways of recovering lost hierarchies and finding new ones, offering authors different views of the same hypertext. The second part helps authors by identifying properties of the hypertext document. Multiple metrics are developed including compactness and stratum . Compactness indicates the intrinsic connectedness of the hypertext, and stratum reveals to what degree the hypertext is organized so that some nodes must be read before others. Several existing hypertexts are used to illustrate the benefits of each technique. The collection of techniques provides a multifaceted view of the hypertext, which should allow authors to reduce undesired structural complexity and create documents that readers can traverse more easily.