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Community detection with the Label Propagation Algorithm: A survey

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  • Garza, Sara E.
  • Schaeffer, Satu Elisa

Abstract

Community detection aims at discovering the structure, behavior, dynamics, and organization of a complex network by finding cohesive groups where nodes (entities) are, in some sense, more similar within the group and groups are in some fashion separated from the other groups. The Label Propagation Algorithm (LPA), which mimics epidemic contagion by spreading labels, is a popular algorithm for this task. Its variants (enhancements, extensions, combinations) seek to improve or to adapt it while preserving the advantages of the original formulation. This survey gathers, classifies, and summarizes LPA-related advances from 2007 to mid 2019. We also experiment with combinations of numerous LPA “building blocks” on two types of small synthetic networks; essentially all of the 13,834 resulting LPA variants have their niche in terms of either higher solution quality or lower computational load with acceptable quality, but none is universally superior.

Suggested Citation

  • Garza, Sara E. & Schaeffer, Satu Elisa, 2019. "Community detection with the Label Propagation Algorithm: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    • Handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119312026
    DOI: 10.1016/j.physa.2019.122058
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    References listed on IDEAS

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    Cited by:

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    4. Jing Yang & Jun Wang & Mengyang Gao, 2023. "Community Evolution Analysis Driven by Tag Events: The Special Perspective of New Tags," Mathematics, MDPI, vol. 11(6), pages 1-18, March.

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