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 fu-st 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 mtrinslc connectedness of the hypertext, and stratum reveals to what degree the hypertext is organized so that some nodes must be read before others. Several exmting hypertext are used to illustrate the benefits of each techmque. The collection of techmques provides a multifaceted view of the hypertext, which should allow authors to reduce undesired structural complexity and create documents that readers can traverse more easdy,
Hypertext systems are being used in many applications because of their flexible structure and the great browsing freedom they give to diverse communities of users. However, this same freedom and flexibility is the cause of one of its main problem: the "lost in hyperspace" problem. One reason for the complexity of hypertext databases is the large number of nodes and links that compose them. To simplify this structure we propose that nodes and links be clustered forming more abstract structures. An abstraction is the concealment of all but relevant properties from an object or concept. One type of abstraction is called an aggregate. An aggregate is a set of distinct concepts that taken together form a more abstract concept. For example, two legs, a trunk, two arms and a head can be aggregate together in a single higher level object called a "body." In this paper we will study the hypertext structure, i.e., the way nodes are linked to each other in order to find aggregates in hypertext databases. Two graph theoretical algorithms will be used: biconnected components and strongly connected components.
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