Higher-order Network Information Propagation Model Based on Social Impact Theory DOI
Xinru Liu, Ruqi Li,

Yu-Rong Song

et al.

Physics Letters A, Journal Year: 2024, Volume and Issue: unknown, P. 129969 - 129969

Published: Oct. 1, 2024

Language: Английский

Social contagions on higher-order community networks DOI
Jiachen Li, Wenjie Li,

Feng Gao

et al.

Applied Mathematics and Computation, Journal Year: 2024, Volume and Issue: 478, P. 128832 - 128832

Published: May 22, 2024

Language: Английский

Citations

9

Dynamics analysis and control of positive–negative information propagation model considering individual conformity psychology DOI

Yan Yu-tao,

Shuzhen Yu, Zhiyong Yu

et al.

Nonlinear Dynamics, Journal Year: 2024, Volume and Issue: 112(18), P. 16613 - 16638

Published: June 26, 2024

Language: Английский

Citations

4

Analysis and Control of Rumor Propagation Model Considering Multiple Waiting Phases DOI Creative Commons

Hai Yan Wu,

Xin Yan, Shengxiang Gao

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(2), P. 312 - 312

Published: Jan. 19, 2025

Rumors pose serious harm to society and exhibit a certain degree of repetitiveness. Existing rumor propagation models often have simple rules neglect the repetitiveness rumors. Therefore, we propose new SCWIR model (susceptible, commented, waited, infected, recovered) by introducing user’s repeated waiting behavior simulate potential for rumors lie dormant spread opportunistically. First, present dynamic equations model, then introduce three influencing factors improve model. Next, solving equilibrium points basic reproduction number, discuss local global stability rumor-free/rumor points. Finally, perform numerical simulations analyze effects different on propagation. The results show that introduction multiple mechanism helps Among suppression strategies, effectiveness, from highest lowest, is as follows: government intervention, information dissemination popularization, accelerated value decay, with intervention playing decisive role. Information can reduce intensity at source.

Language: Английский

Citations

0

Beyond Boundaries: Capturing Social Segregation on Hypernetworks DOI
Andrea Failla, Giulio Rossetti,

Francesco Cauteruccio

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 40 - 55

Published: Jan. 1, 2025

Language: Английский

Citations

0

Structure-and-embedding-based centrality on network fragility in hypergraphs DOI

L.W. Chang,

Tian Qiu,

Guang Chen

et al.

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2025, Volume and Issue: 35(3)

Published: March 1, 2025

Revealing the critical nodes is crucial to maintain network safety. Various methods have been proposed identify vital and, recently, generalized from ordinary networks hypergraphs. However, many existing did not consider both hypergraph structure and embedding. In this article, we investigate two topological structural centralities by considering common hyperedges a embedding centrality based on representation learning. Four improved are only node embedding, joint of nature. The fragility investigated for six real datasets. found outperform baseline in five hypergraphs, incorporating feature into can greatly improve performance single structure-based centralities. obtained results heuristically understood similarity analysis embeddings.

Language: Английский

Citations

0

How Can Individuals Develop Critical Thinking Skills to Evaluate Online Information DOI

Ya-feng Xiong,

Zhanni Luo

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 165 - 186

Published: March 20, 2025

The rise of the internet and digital platforms has revolutionized production sharing information, allowing individuals to express their opinions quickly effortlessly. However, ease creating disseminating content also resulted in spread misleading irrelevant information. To address this challenge, chapter outlines five criteria for evaluating credibility, three steps assessing relevance, six strategies enhance evaluation process. These tools aim foster critical thinking improve literacy, enabling manage complexities online information effectively.

Language: Английский

Citations

0

Phase transition in infection scale and the impact of user characteristics under the herd effect in information propagation DOI
Fuzhong Nian, J. Feng

The European Physical Journal Plus, Journal Year: 2025, Volume and Issue: 140(5)

Published: May 12, 2025

Language: Английский

Citations

0

A topic dissemination model based on hypernetwork DOI Creative Commons
Zhongming Han, Yan Liu,

Shichun Zhang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 15, 2025

Language: Английский

Citations

0

A public opinion propagation model for technological disasters DOI Creative Commons
Yi Zhang, Wanjie Tang, Ting Ni

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 6, 2025

Public opinion on technological disasters is influenced by unique factors and characteristics. Based the infectious disease model, this paper develops a public dissemination model for disasters, considering such as disaster severity, government response, accountability, impact of both positive negative media content. Using differential equation stability theory, we analyze existence free propagation equilibrium point point. The next-generation matrix method applied to calculate threshold, revealing that accountability are key in spread opinion. Sensitivity analyses examine how these affect dynamics. A case study Shiyan gas explosion Hubei Province presented, with microblog data used parameters. proposed compared two other models, demonstrating viability effectiveness developed model. also show well-handled responses can help calm opinion, even cases where lacking. Finally, policy suggestions offered enhance management during disasters.

Language: Английский

Citations

0

An opinion evolution model for online social networks considering higher-order interactions DOI Creative Commons
Quan Liu,

Yuekang Yao,

Meimei Jia

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0321718 - e0321718

Published: April 16, 2025

As the number of users in online social networks increases, diffusion information and users’ opinions on events become more complex, making it difficult for traditional complex to accurately capture their characteristics patterns. To address this, this paper proposes an network opinion evolution model that accounts higher-order interactions. The incorporates effects group interactions introduces acceptance, non-commitment, rejection dimensions from judgment theory. Different approaches, such as neutrality, contrastive rejection, are adopted when individuals exchange with neighbors. Through numerical simulations, is shown significantly enhance speed coverage propagation. When interaction appropriate, increasing average size hyperedges contributes formation consensus. In contrast, simply nodes involved has a limited impact consensus formation. This work provides theoretical model-based foundation better understanding dynamics networks.

Language: Английский

Citations

0