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A Regularized Stochastic Block Model for the robust community detection in complex networks.

Xiaoyan Lu1, Boleslaw K Szymanski2,3

  • 1Social and Cognitive Networks Academic Research Center and Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.

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Summary
This summary is machine-generated.

This study introduces a new network model that guides inference algorithms to identify specific assortative or disassortative structures in network data. This regularized model reliably finds desired partitions, unlike traditional methods that may converge to incorrect structures.

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Area of Science:

  • Network science
  • Statistical modeling
  • Data analysis

Background:

  • The stochastic block model (SBM) generates random graphs with various network partitions.
  • Current SBM inference algorithms identify the most likely partition but do not control for structure type (assortative vs. disassortative).
  • Undesired partitions can be discovered when community structures are weak or absent.

Purpose of the Study:

  • To introduce a novel regularized stochastic block model.
  • To guide inference algorithms towards specific assortative or disassortative network structures.
  • To improve the reliability and speed of community detection in network data.

Main Methods:

  • Developed a new model by constraining nodes' internal degree ratios in the objective function.
  • Employed Markov chain Monte Carlo (MCMC) and other inference algorithms.
  • Experimentally validated the model's performance on network data.

Main Results:

  • The regularized model successfully directs inference algorithms to find either assortative or disassortative structures based on a single parameter.
  • The new model demonstrates reliable and fast convergence to the desired network partition.
  • In contrast, traditional degree-corrected SBM algorithms often converge to undesired disassortative partitions when assortative structures are weak.

Conclusions:

  • The proposed regularized model offers a powerful tool for controlling community detection in network analysis.
  • This approach enhances the accuracy and efficiency of identifying specific network structures.
  • It addresses limitations of existing SBM inference methods, particularly in challenging network scenarios.