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Network reconstruction using node similarity is affected by epidemic spread rates. A new temporal similarity metric improves accuracy by incorporating time data, enhancing network analysis for epidemic modeling.

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Area of Science:

  • Network science
  • Epidemiology
  • Data science

Background:

  • Node similarity is crucial for understanding real-world network growth.
  • Existing similarity metrics are used to infer network structures from observed processes like epidemic spreading.
  • The accuracy of these network reconstruction methods can be limited by factors such as the infection rate during propagation.

Purpose of the Study:

  • To investigate the impact of epidemic spreading dynamics on the accuracy of network reconstruction using node similarity metrics.
  • To develop an improved method for network reconstruction that addresses the limitations of existing approaches.

Main Methods:

  • Applying various node similarity metrics to reconstruct networks based on simulated epidemic spreading data.
  • Analyzing the relationship between reconstruction accuracy and the infection rate parameter of the spreading process.
  • Proposing and implementing a novel temporal similarity metric that integrates time information from the spreading events.

Main Results:

  • Reconstruction accuracy of standard similarity metrics is highly sensitive to the infection rate.
  • A significant drop in accuracy, nearing zero, was observed for some metrics within specific infection rate ranges.
  • The proposed temporal similarity metric demonstrated a remarkable improvement in reconstruction accuracy compared to existing methods.

Conclusions:

  • The effectiveness of node similarity for network reconstruction is contingent on the spreading process parameters, particularly the infection rate.
  • Standard similarity metrics are insufficient for accurate network reconstruction under certain epidemic spreading conditions.
  • Incorporating temporal information via a temporal similarity metric significantly enhances the ability to reconstruct underlying networks from observed spreading data.