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Related Experiment Videos

Evolution of document networks.

Filippo Menczer1

  • 1School of Informatics, Indiana University, Bloomington, IN 47408, USA. fil@indiana.edu

Proceedings of the National Academy of Sciences of the United States of America
|January 30, 2004
PubMed
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Existing network growth models overlook document content, failing to predict textual similarity. A new model incorporating both popularity and content accurately predicts network characteristics in web pages and scientific literature.

Area of Science:

  • Network Science
  • Information Science
  • Computational Social Science

Background:

  • Understanding the growth of complex networks, like the World Wide Web, is crucial for information retrieval and analysis.
  • Current models often focus on link structure but neglect the content of the documents within the network.
  • This oversight limits the accurate prediction of network properties, particularly concerning the relationships between linked content.

Purpose of the Study:

  • To investigate the limitations of existing network growth models in capturing the distribution of textual similarity.
  • To propose and validate a novel network growth model that incorporates both link popularity and node content.
  • To demonstrate the model's ability to accurately predict key characteristics of real-world information networks.

Main Methods:

Related Experiment Videos

  • Analysis of existing network growth models and their predictive capabilities regarding link structure and content similarity.
  • Development of a new generative model for information networks that considers both node popularity and textual content for link formation.
  • Empirical validation of the proposed model using datasets from the World Wide Web and scientific literature.

Main Results:

  • Existing models fail to accurately reproduce the observed distribution of textual similarity between linked documents.
  • The proposed model, which integrates document content and popularity, successfully predicts both the degree distribution and the textual similarity distribution.
  • The model demonstrates high accuracy in simulating the growth patterns of both web page and scientific literature networks.

Conclusions:

  • Document content is a critical factor in the emergent topology of information networks, alongside link popularity.
  • A more realistic understanding of network growth requires models that account for both structural and content-based attributes.
  • The proposed content-aware model offers a significant improvement for analyzing and predicting the behavior of large-scale information networks.