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Related Experiment Video

Updated: Jan 3, 2026

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

Misinformation spreading on correlated multiplex networks.

Jiajun Xian¹, Dan Yang², Liming Pan²

¹School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Chaos (Woodbury, N.Y.)

|November 30, 2019

Summary

This summary is machine-generated.

Misinformation spreads rapidly on online social networks. This study introduces a multiplex network model and theory to analyze and predict misinformation outbreaks, identifying key factors that accelerate spread.

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Area of Science:

Network Science
Information Science
Computational Social Science

Background:

Online social networks facilitate rapid misinformation spread, impacting socioeconomic systems.
Existing research lacks systematic theoretical studies on misinformation dynamics in correlated multiplex networks.

Purpose of the Study:

To propose a multiplex network-based misinformation spreading model.
To develop a theoretical framework for analyzing spreading dynamics and outbreak thresholds.
To investigate the influence of various parameters on misinformation propagation.

Main Methods:

Developed a multiplex network model incorporating multiple information sources per individual.
Employed heterogeneous edge-based compartmental theory for dynamic analysis.
Utilized stability analysis to determine the misinformation outbreak threshold (βc).

Main Results:

Misinformation outbreak size (R(∞)) increases with transmission probability (β) above the outbreak threshold (βc).
Higher average degrees, degree heterogeneity, and positive interlayer correlation decrease βc, accelerating outbreaks.
Degree heterogeneity and positive interlayer correlation influence R(∞) differently based on β values.

Conclusions:

The proposed model and theoretical analysis provide a framework for understanding misinformation spread on multiplex networks.
Findings offer insights into predicting and mitigating the impact of online misinformation.
The study highlights the importance of network structure and parameters in controlling information cascades.