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Effects of interlayer alternation on information diffusion on directed multiplex higher-order networks.
Dandan Zhao1, Jiayan Luo1, Bo Zhang2
1School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, Zhejiang, China.
Chaos (Woodbury, N.Y.)
|January 2, 2026
Summary
We developed a new model for information diffusion in complex social networks. Enhancing directional group interactions can improve information spread, overcoming suppression seen in intermediate network alternation.
Area of Science:
- Network Science
- Information Diffusion Models
- Computational Social Science
Background:
- Social networks feature complex, multi-channel information transmission.
- Existing models often overlook network directionality and higher-order structures.
- Information diffusion is influenced by both pairwise and group interactions.
Purpose of the Study:
- To propose a novel Susceptible-Adopted-Recovered (SAR) model for information diffusion.
- To analyze diffusion on directed, multiplex, higher-order networks with group interactions.
- To investigate the impact of directionality and interlayer alternation on diffusion dynamics.
Main Methods:
- Developed a SAR model incorporating dyadic and group-level interactions across network layers.
- Embedded directionality within higher-order structures using a tunable weight parameter.
- Conducted simulations to analyze diffusion size dependence on interlayer alternation probability.
Main Results:
- Information diffusion size shows non-monotonic dependence on interlayer alternation probability.
- Intermediate alternation regimes can suppress diffusion, creating a non-monotonic effect.
- Increased directional transmission within higher-order structures mitigates suppression and enhances diffusion.
Conclusions:
- Directional group interactions and interlayer alternation are crucial for accurate diffusion modeling.
- Structural and temporal heterogeneities jointly regulate information diffusion in multilayer social systems.
- The proposed framework provides insights into optimizing information spread in complex networks.
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