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Link Definition Ameliorating Community Detection in Collaboration Networks.

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Summary

This study introduces a novel "collaboration score" to quantify research partnerships, improving the extraction and analysis of scientific collaboration networks. Network quality and community detection significantly depend on the chosen collaboration score threshold.

Keywords:
collaboration networkcommunity detection analysisdata analysisdata-to-networknetwork interactions

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Area of Science:

  • Bibliometrics
  • Network Science
  • Data Mining

Background:

  • Collaboration networks reveal community structures in research.
  • Existing methods often simplify collaboration to a binary existence, ignoring interaction quality.
  • Diverse features, like contribution amounts, characterize the quality of scientific interactions.

Purpose of the Study:

  • To develop a method for extracting collaboration networks that incorporates interaction quality features.
  • To introduce a quantifiable 'collaboration score' for measuring collaborator relationships.
  • To evaluate the impact of this score on network analysis and community detection.

Main Methods:

  • Defined a 'collaboration score' to quantify the strength of collaboration between individuals.
  • Utilized a dataset of researchers, co-authorship, papers, prototypes, and intellectual properties (IP).
  • Applied network analysis metrics (clustering coefficient, density, centrality) and community detection algorithms to evaluate extracted networks.

Main Results:

  • Collaboration networks were successfully extracted using the proposed 'collaboration score'.
  • The quality of detected communities varied significantly based on the selected collaboration score threshold.
  • Network analysis metrics provided insights into the structure of collaboration networks.

Conclusions:

  • The proposed 'collaboration score' offers a nuanced approach to building collaboration networks.
  • Threshold selection is critical for accurate network representation and effective community detection.
  • This method enhances the understanding of research collaboration patterns and community structures.