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Normalized modularity optimization method for community identification with degree adjustment.

Shuqin Zhang1, Hongyu Zhao2

  • 1Center for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China.

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|December 17, 2013
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Summary
This summary is machine-generated.

This study introduces a new modularity definition based on average degree for network community identification. This approach improves upon traditional methods, especially for networks with unbalanced structures, enhancing community detection accuracy.

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Community identification is a fundamental problem in network analysis.
  • Modularity optimization is a widely used method but suffers from resolution limits and issues with unbalanced network structures.
  • Existing methods rely on comparing edge counts in observed networks to random networks, which is unsuitable for certain network types.

Purpose of the Study:

  • To address the limitations of traditional modularity optimization in network community detection.
  • To propose a novel modularity definition based on average degree within communities.
  • To introduce a degree-adjusted approach for improved performance on networks with unbalanced structures.

Main Methods:

  • Defined a new modularity metric based on the average degree of nodes within communities.
  • Formulated modularity by comparing the sum of average degrees in observed networks to equivalent random networks.
  • Developed and analyzed a degree-adjusted method to further enhance community detection in unbalanced networks.

Main Results:

  • The proposed average degree-based modularity definition is more suitable for analyzing networks with unbalanced structures.
  • Theoretical properties of the degree-adjusted method were analyzed.
  • Numerical experiments on artificial and real networks demonstrated superior performance compared to existing methods.

Conclusions:

  • Average degree is a crucial factor in network community identification.
  • The proposed average degree-based and degree-adjusted modularity methods offer improved accuracy and robustness in community detection.
  • These novel approaches advance the field of network analysis, particularly for complex network structures.