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Finding local community structure in networks.

Aaron Clauset1

  • 1Department of Computer Science, University of New Mexico, Albuquerque, New Mexico 87131, USA. aaron@cs.unm.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 4, 2005
PubMed
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This study introduces a novel algorithm for inferring local community structure in networks without needing complete graph knowledge. The method efficiently identifies community hierarchies by exploring networks vertex by vertex.

Area of Science:

  • Network Science
  • Graph Theory
  • Computational Physics

Background:

  • Inferring global community structure in networks is a significant challenge.
  • Existing algorithms typically require complete knowledge of the network graph.
  • This limitation hinders community detection in large, dynamic, or partially observed networks.

Purpose of the Study:

  • To develop a measure for local community structure.
  • To create an algorithm for inferring community hierarchies around a specific vertex.
  • To enable community detection without requiring full graph information.

Main Methods:

  • Defined a measure of local community structure.
  • Developed a vertex-by-vertex graph exploration algorithm to infer community hierarchies.

Related Experiment Videos

  • Analyzed algorithmic time complexity: O(k^2d) for general graphs, O(k) for costly vertex exploration.

    • Main Results:

    • The algorithm successfully infers local community hierarchies by exploring the graph incrementally.
    • Performance on computer-generated graphs approximates that of global knowledge-based algorithms.
    • Demonstrated practical application in extracting local clustering from a large online retailer's recommender network.

    Conclusions:

    • The proposed local community detection algorithm offers an efficient alternative to global methods.
    • It provides meaningful insights into network structure, even with incomplete graph data.
    • The approach is applicable to real-world large-scale networks, such as recommender systems.