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Fast and accurate detection of spread source in large complex networks.

Robert Paluch1, Xiaoyan Lu2, Krzysztof Suchecki3

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This study introduces a Gradient Maximum Likelihood Algorithm (GMLA) to efficiently locate spread sources in complex networks. GMLA improves accuracy and reduces computational complexity compared to the existing Pinto, Thiran, and Vetterli Algorithm (PTVA).

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Area of Science:

  • Network Science
  • Epidemiology
  • Computer Science

Background:

  • Identifying spread origins in complex networks is crucial for applications like epidemic control and rumor tracking.
  • Existing methods, such as the Pinto, Thiran, and Vetterli Algorithm (PTVA), utilize observer data but can be computationally intensive.
  • Observer information quality can vary, impacting the accuracy of source localization.

Purpose of the Study:

  • To develop a more efficient and accurate algorithm for locating spread sources in complex networks.
  • To address the limitations of existing methods by incorporating observer information quality.
  • To reduce the computational complexity of spread source localization.

Main Methods:

  • Proposed a Gradient Maximum Likelihood Algorithm (GMLA) that prioritizes high-quality observer data.
  • GMLA filters out observers with low-quality information (late spread detection times).
  • Evaluated GMLA's performance against PTVA on synthetic and real-world networks (Gnutella), considering unknown spreader identities.

Main Results:

  • GMLA significantly reduces computational complexity from O(N^α) to O(N^2 log N).
  • GMLA demonstrates superior localization accuracy compared to PTVA, especially on scale-free networks.
  • The algorithm performs effectively even when the identities of spreaders are unknown to observers.

Conclusions:

  • The Gradient Maximum Likelihood Algorithm (GMLA) offers a more efficient and accurate approach to spread source localization in complex networks.
  • Prioritizing high-quality observer data is key to improving localization performance.
  • GMLA provides a valuable advancement for applications requiring rapid and precise identification of spread origins.