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Comparison of observer based methods for source localisation in complex networks.

Łukasz G Gajewski1, Robert Paluch2, Krzysztof Suchecki2

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This study compares observer-based methods for source localization in complex networks. The Pearson correlation and multiple path analysis methods are most effective, with the best choice depending on the infection rate.

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Area of Science:

  • Network Science
  • Epidemiology
  • Information Science

Background:

  • Locating the origin of spreading phenomena in complex networks is crucial for epidemic control and identifying misinformation sources.
  • Numerous methods exist, but direct comparisons of their efficiency are lacking.

Purpose of the Study:

  • To provide a comprehensive comparison of observer-based source localization methods on complex networks.
  • To analyze the impact of network topology, observer density, infection rate, and observer placement on method precision.

Main Methods:

  • Comparison of several observer-based source localization techniques.
  • Utilizing exact spread arrival times at observer nodes.
  • Investigating performance across varying network structures and parameters.

Main Results:

  • The Pearson correlation-based method and the multiple path analysis method demonstrate the highest effectiveness on both synthetic and real network topologies.
  • The Pearson correlation method excels at low infection rates, while the multiple path analysis method is superior at higher rates.

Conclusions:

  • The findings facilitate informed selection of source localization methods for practical applications and future research.
  • Method choice depends critically on the specific network characteristics and infection dynamics.