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Finding communities in sparse networks.

Abhinav Singh1, Mark D Humphries1

  • 1Faculty of Life Sciences, University of Manchester.

Scientific Reports
|March 7, 2015
PubMed
Summary

New spectral methods using reluctant backtracking operators improve community detection in sparse networks by accounting for hanging trees, outperforming non-backtracking methods in specific cases.

Area of Science:

  • Network science
  • Graph theory
  • Data mining

Background:

  • Classic spectral methods for community detection struggle with sparse networks.
  • Non-backtracking random walks offer improvements but overlook network 'hanging trees'.

Purpose of the Study:

  • Introduce reluctant backtracking operators to improve community detection in sparse networks.
  • Address limitations of non-backtracking methods by incorporating hanging tree information.

Main Methods:

  • Developed reluctant backtracking operators with a small probability of returning to the previous node.
  • Analyzed the spectrum of these operators and their performance on networks.
  • Compared performance against non-backtracking operators on benchmark and real-world networks.

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Main Results:

  • Reluctant backtracking operators successfully detect communities in sparse networks where non-backtracking methods fail.
  • Performance is comparable to non-backtracking methods on standard benchmarks.
  • The spectrum of the reluctant backtracking operator approximates modularity optimization.

Conclusions:

  • Reluctant backtracking operators offer a more robust spectral approach for community detection in sparse networks.
  • Accounting for hanging trees enhances community detection capabilities.
  • Network normalization is a key factor for operator performance on real-world data.