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Evaluating network communities lacks objective criteria. This study introduces a graph clustering framework using precision and recall, enabling comparative analysis of clustering methods and highlighting the trade-off between community density and connectivity.

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Area of Science:

  • Graph theory
  • Network analysis
  • Data mining

Background:

  • Lack of consensus on objective criteria for evaluating network communities.
  • Difficulty in comparing diverse graph clustering methods due to undefined evaluation metrics.
  • Need for a standardized framework to assess community detection algorithms.

Purpose of the Study:

  • Propose a graph clustering framework using precision and recall metrics.
  • Formalize criteria for densely connected communities and weakly connected inter-community relationships.
  • Enable objective comparison of graph clustering methods.

Main Methods:

  • Formalizing community density with precision and inter-community connectivity with recall.
  • Analyzing the antagonistic relationship between precision and recall for graph clustering.
  • Developing a framework for comparing clustering method performance, even without ground truth.

Main Results:

  • Precision and recall are often antagonistic in graph clustering, requiring a subjective compromise.
  • The proposed framework allows for the comparison of five state-of-the-art clustering methods.
  • A new family of clustering methods inspired by the precision-recall framework was introduced.

Conclusions:

  • The proposed framework provides a quantifiable and interpretable method for evaluating network communities.
  • The precision-recall trade-off necessitates a user-defined balance for optimal clustering.
  • This approach facilitates more rigorous and comparative studies in graph clustering research.