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Hypertext macrodynamics

  • Valery M. Chelnokov
  • Victoria L. Zephyrova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1015)

Abstract

The authors consider the situation in which a hypertext network grows due to a stream of new nodes. The network's evolution includes not only the growth of its node-link microstructure, but also the processes of becoming, maintaining, and collapsing unities of meaning in the population of nodes as well. The notion of hypertext macrodynamics is introduced to refer to these processes. In the authors' hypertext system (SMIsC), the unities, or coherence clusters, are system-detectable network regions, and the system provides users with nagivational assistance in building discourse-like trails. As the authors report, this system enabled them to imitate the microstructure growth caused by a long sequence of incoming nodes and to observe a real network's macrodynamics. Observed values included a node's “thematic potential” and a node's “Ioad/activity”, two indicators of a node's macrostate. Experimental data include two basic forms: time profiles of the network's “spatial sections” and spatial profiles of the network's “time sections”. The former are the two indicators' time series, while the latter are distributions of the two indicators' responses over populations of nodes. The data show that most nodes get individualized profiles in macrodynamics. The “macrohistory” trajectories of nodes range from very simple to very complicated. Many nodes tend to behave n a random, unpredictable way. In this connection, the authors refer to modern studies on chaotic systems. The authors believe that the possibility for users to control chaos in macrodynamics, i.e., in the creation of meaning, reveal a new kind of hypertext interactivity.

Keywords

Chaotic System Spatial Profile Thematic Cluster Switch Node Local Coherence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Valery M. Chelnokov
    • 1
  • Victoria L. Zephyrova
    • 1
  1. 1.SSTC-GINTECHMoscowRussia

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