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Updated: Feb 13, 2026

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Decoding communities in networks.

Filippo Radicchi1

  • 1Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA.

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Summary
This summary is machine-generated.

This study reframes community detection as a communication problem. Applying coding theory reveals a decodability bound, explaining current algorithm performance and suggesting limited room for improvement in network community identification.

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Area of Science:

  • Network Science
  • Information Theory
  • Computer Science

Background:

  • Community detection is crucial for understanding network structure.
  • Existing methods often rely on stochastic network models.
  • An information-theoretical perspective offers novel insights.

Purpose of the Study:

  • To apply coding theory to network community detection.
  • To develop new community detection algorithms.
  • To establish a theoretical performance bound for community detection.

Main Methods:

  • Interpreting community detection as a communication task over a noisy channel.
  • Utilizing state-of-the-art decoding techniques from coding theory.
  • Applying Shannon's noisy-channel coding theorem to derive a decodability bound.

Main Results:

  • A family of quasi-optimal community detection algorithms was generated.
  • A decodability bound was established, quantifying tolerable noise for perfect community detection.
  • This bound accurately predicts the performance of leading community detection algorithms on synthetic benchmarks.

Conclusions:

  • Coding theory provides powerful tools for network community detection.
  • The decodability bound suggests current algorithms are near-optimal.
  • Future improvements in community detection may be limited.