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Optimal deployment of resources for maximizing impact in spreading processes.

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Optimizing resource allocation for controlling network spread requires balancing network topology and dynamic processes. A new scalable message-passing framework effectively addresses these complex challenges in real-world scenarios.

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dynamic resource allocationinfluence maximizationmessage-passing algorithmsmitigation of epidemic outbreakoptimal control of spreading processes

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Area of Science:

  • Network Science
  • Complex Systems
  • Computational Social Science

Background:

  • Controlling spreading processes on networks is crucial for information dissemination, disease mitigation, and financial stability.
  • Topological approaches alone are insufficient for realistic scenarios with heterogeneous interactions and dynamic interventions.
  • Resource allocation must consider the interplay between network structure and the dynamics of spread.

Purpose of the Study:

  • To develop a universal analytical framework for optimizing resource allocation in controlling network spreading processes.
  • To address the limitations of purely topological methods in dynamic, heterogeneous network environments.
  • To demonstrate a scalable dynamic message-passing approach for effective intervention strategies.

Main Methods:

  • Developed a scalable dynamic message-passing approach.
  • Formulated the problem as a universal analytical framework.
  • Applied the method to diverse real-world network examples.

Main Results:

  • The proposed framework effectively integrates network topology and spreading dynamics for optimal resource allocation.
  • Demonstrated the method's efficacy across various real-world applications, including information diffusion and disease control.
  • Showcased the scalability and practical applicability of the dynamic message-passing technique.

Conclusions:

  • The universal analytical framework provides a robust solution for resource-constrained control of network spreading.
  • The dynamic message-passing method offers a scalable and effective approach for real-world intervention strategies.
  • This work advances the understanding and practical management of complex spreading phenomena in networked systems.