Strategic distribution of seeds to support diffusion in complex networks

Area of Science:

• Social Network Analysis
• Information Diffusion Dynamics
• Computational Social Science

Background:

• Traditional diffusion models assume simultaneous seed activation.
• This approach may not maximize coverage or reflect real-world continuous seeding strategies.
• Identifying influential spreaders is crucial but insufficient for dynamic seeding.

Purpose of the Study:

• To investigate optimal strategies for distributing seeds over time within a budget.
• To analyze the impact of dynamic seeding on diffusion coverage and speed.
• To determine if a universally optimal seeding strategy exists.

Main Methods:

• Theoretical analysis to prove the NP-hard nature of the problem.
• Numerical evaluation of various dynamic seeding strategies across different network topologies.
• Comparison of strategies based on maximizing diffusion coverage and minimizing spread time.

Main Results:

• No single universally optimal seeding strategy exists.
• Optimal seed distribution over time is an NP-hard problem.
• The most effective seeding strategy is network-topology dependent and influenced by spreading parameters.

Conclusions:

• Dynamic seeding strategies are essential for maximizing diffusion.
• Network-specific analysis is required to determine optimal budget allocation for seeding.
• Findings are applicable to marketing, political campaigns, and other diffusion scenarios.