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Modelling indirect interactions during failure spreading in a project activity network.

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This study introduces a model accounting for both direct and subsequent exposure in network propagation. Findings reveal subsequent exposure significantly impacts spreading dynamics and the emergence of hidden influentials.

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Area of Science:

  • Network Science
  • Complex Systems
  • Computational Modeling

Background:

  • Spreading phenomena, such as disease epidemics and financial risk, occur in networked systems.
  • Current models often focus on direct, pairwise interactions, neglecting indirect exposure effects.
  • Understanding propagation dynamics is crucial for managing systemic risks.

Purpose of the Study:

  • To develop a model incorporating both direct and subsequent exposure in propagation dynamics.
  • To investigate the impact of subsequent exposure on spreading events in a real-world engineering project.
  • To identify and analyze 'hidden influentials' in large-scale spreading processes.

Main Methods:

  • Development of a novel computational model for propagation analysis.
  • Application of the model to a real-world engineering project dataset.
  • Evaluation of the influence of direct versus subsequent exposure on propagation metrics.

Main Results:

  • Subsequent exposure significantly affects final spreading size, propagation rate, and event structure.
  • Demonstration of 'hidden influentials' and their link to exposure types.
  • Quantification of the impact of indirect interactions on failure propagation.

Conclusions:

  • Subsequent exposure is a critical factor in network propagation dynamics.
  • Existing models may underestimate risks by omitting indirect exposure effects.
  • The findings provide insights for more accurate network modeling and risk management.