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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.

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|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.

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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.