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Block-corrected modularity for community detection.

Hasti Narimanzadeh1, Takayuki Hiraoka1, Mikko Kivelä1

  • 1Aalto University, Department of Computer Science, 00076 Espoo, Finland.

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|September 16, 2025
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Summary
This summary is machine-generated.

We introduce block-corrected modularity to uncover hidden community structures in complex networks. This method effectively reveals communities masked by known attributes, outperforming existing techniques on synthetic and real-world data.

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Area of Science:

  • Network Science
  • Data Mining
  • Complex Systems Analysis

Background:

  • Community structures in complex networks are often influenced by both known and unknown node attributes.
  • Distinguishing between community structures driven by different attribute types is crucial for accurate network analysis.
  • Existing methods may fail to identify underlying communities when influenced by confounding block structures.

Purpose of the Study:

  • To develop a novel modularity measure, block-corrected modularity, to identify community structures masked by known attributes.
  • To analytically and empirically demonstrate the effectiveness of block-corrected modularity in revealing hidden community structures.
  • To provide efficient algorithms for maximizing the proposed modularity and apply it to real-world networks.

Main Methods:

  • Proposing a block-corrected modularity that discounts existing block structures within a network.
  • Analytical derivation of the modularity's effectiveness in a simple network model.
  • Developing spectral and Louvain-inspired algorithms for modularity maximization.
  • Validation on synthetic network models and real-world citation networks (OpenAlex data).

Main Results:

  • Block-corrected modularity successfully identifies community structures driven by unknown attributes in synthetic models.
  • The proposed method outperforms existing techniques that utilize different null models.
  • Efficient algorithms demonstrate strong performance in maximizing the block-corrected modularity.
  • Application to citation networks corrects for temporal citation patterns, revealing underlying research communities.

Conclusions:

  • Block-corrected modularity is a robust method for uncovering latent community structures in complex networks.
  • The developed algorithms provide efficient means for applying this novel modularity measure.
  • This approach enhances the analysis of real-world networks by accounting for confounding factors like temporal dynamics.