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Summary
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We studied information diffusion in networks using the linear threshold model. Optimal network structures for maximizing diffusion depend on network properties, with assortative core-periphery graphs being most efficient for global cascades.

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Area of Science:

  • Network Science
  • Information Diffusion
  • Statistical Physics

Background:

  • Understanding information spread in complex networks is crucial.
  • The linear threshold model and stochastic block model are key frameworks for network analysis.

Purpose of the Study:

  • To identify optimal network structures for information diffusion.
  • To determine network configurations that minimize the cost of triggering global cascades.

Main Methods:

  • Utilizing the linear threshold model to simulate information diffusion.
  • Analyzing networks generated by the stochastic block model with a two-community structure.
  • Investigating network properties like assortativity, modularity, and core-periphery structures.

Main Results:

  • Optimal network structures maximizing diffusion can be assortative, core-periphery, or disassortative under certain constraints.
  • Minimal cost structures for global cascades are assortative, resembling core-periphery graphs with a dense core and sparse periphery.

Conclusions:

  • Network topology significantly impacts information diffusion efficiency.
  • Core-periphery structures are highly effective for initiating widespread information cascades with minimal initial spread.