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Updated: Nov 27, 2025

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Patent citation network analysis: A perspective from descriptive statistics and ERGMs.

Manajit Chakraborty1, Maksym Byshkin1, Fabio Crestani1

  • 1Faculty of Informatics, Universitá della Svizzera italiana, Lugano, Switzerland.

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|December 3, 2020
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Patent citation network analysis reveals that social factors like homophily and transitivity significantly influence patent citations. Exponential Random Graph Models (ERGMs) offer new insights into these citation networks.

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Area of Science:

  • Bibliometrics
  • Network Science
  • Patent Analysis

Background:

  • Patent citation analysis is increasingly important for understanding innovation.
  • Previous studies often focused on descriptive statistics rather than network mechanisms.

Purpose of the Study:

  • To analyze patent citation networks from a statistical perspective.
  • To explore the social and technical factors influencing patent citations using advanced statistical models.
  • To provide insights into European patent citation patterns.

Main Methods:

  • Collection and analysis of extensive patent and citation data.
  • Descriptive analysis of most cited patents, innovative companies, and network structures.
  • Application of Exponential Random Graph Models (ERGMs) to model citation networks.

Main Results:

  • Identification of key structural properties within patent citation networks.
  • Demonstration that social properties (homophily, transitivity) and technical aspects (language, categories) significantly affect citations.
  • Analysis of sector-specific citation patterns and their relation to sector size.

Conclusions:

  • ERGMs provide a powerful tool for modeling network mechanisms in large-scale patent data.
  • Social and technical factors play crucial roles in shaping patent citation networks.
  • This study offers novel insights into the dynamics of European patent citations.