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Related Experiment Videos

Growing and navigating the small world Web by local content.

Filippo Menczer1

  • 1Department of Management Sciences, University of Iowa, Iowa City, IA 52242, USA. filippo-menzer@uiowa.edu

Proceedings of the National Academy of Sciences of the United States of America
|October 17, 2002
PubMed
Summary

This study proposes a Web growth model that accurately predicts the distribution of Web page degrees. The model uses textual content and local knowledge, revealing insights into web structure and search engine development.

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Area of Science:

  • Web science
  • Network analysis
  • Information retrieval

Background:

  • Understanding the scale-free distribution of Web hypertext degree is crucial for web structure analysis and search engine development.
  • Investigating the relationship between web page linkage and content topology is essential for realistic modeling.
  • Existing models often lack realistic assumptions about author behavior and efficient page discovery.

Purpose of the Study:

  • To model the scale-free distribution of Web hypertext degree under realistic assumptions.
  • To explore the connection between linkage and content topology of Web pages.
  • To propose a Web growth model predicting page degree distribution based on textual content.

Main Methods:

  • Analyzing the relationship between a text-induced distance metric and a link-based neighborhood probability distribution.

Related Experiment Videos

  • Identifying a phase transition between content-determined and power-law decaying linkage.
  • Developing a Web growth model incorporating textual content and local degree knowledge.

    • Main Results:

    • A phase transition was observed where linkage is either not determined by content or decays according to a power law.
    • The proposed Web growth model accurately predicts the distribution of Web page degree.
    • A similar phase transition was found between linkage and semantic distance, with an exponential decay tail.

    Conclusions:

    • Decentralized Web navigation algorithms can efficiently discover paths using textual and/or categorical cues.
    • The proposed model provides a realistic framework for understanding Web growth and structure.
    • Insights gained can inform the development of more effective search engines.