UHIR: An effective information dissemination model of online social hypernetworks based on user and information attributes

Gong, Yunchao; Wei, Min; Liang, Wei; Hu, Feng; Zhang, Zi-Ke

doi:10.1016/j.ins.2023.119284

Cited by 7 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Net-E-SFI [79], S1S2R1R2 [80], Hyper-ILSR [82], SEIR [83], IFCD [85], UHIR [86] In line with reality, highlighting the influence of network topology on information dissemination. Effectively reflects differences among nodes.…”

Section: Heterogeneous Networkmentioning

confidence: 63%

“…Tong et al [85] analyze the random perturbations in the propagation process, which consider user heterogeneity and dynamic network environments, demonstrating the feasibility of the model. Gong et al [86] propose a UH IR model which combines the super-network theory and SEIR model to study user and information attributes' influence; the proposed model establishes new research directions for hyper-networks.…”

Section: In Heterogeneous Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Information Dissemination Model, Datasets, and Insight

Liu,

Zhang,

Shi

et al. 2023

Mathematics

View full text Add to dashboard Cite

Information dissemination refers to how information spreads among users on social networks. With the widespread application of mobile communication and internet technologies, people increasingly rely on information on the internet, and the mode of information dissemination is constantly changing. Researchers have performed various studies from mathematical modeling and cascade prediction perspectives to explore the previous problem. However, lacking a comprehensive review of the latest information dissemination models hinders scientific development. As a result, it is essential to review the latest models or methods. In this paper, we review information dissemination models from the past three years and conduct a detailed analysis, such as explanatory and predictive models. Moreover, we provide public datasets, evaluation metrics, and interface tools for researchers focusing more on algorithm design and modeling. Finally, we discuss the model application and future research directions. This paper aims to understand better the research progress and development trends for beginners and guide future research endeavors. We believe this article will attract more researchers’ interest and attention to the information dissemination field on social networks.

show abstract

Section: Heterogeneous Networkmentioning

confidence: 63%

Section: In Heterogeneous Networkmentioning

confidence: 99%

A Survey of Information Dissemination Model, Datasets, and Insight

Liu,

Zhang,

Shi

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…Nie et al(2022) considered the individual contact ability distribution, and found that the larger the mean of the individual contact ability distribution, the higher the information prevalence. Gong et al(2023) proposed the UHIR (Unknown, Hibernated, Infected, Removed) model, which combines the hyper-network model with the SEIR model to establish an online social hyper-network information dissemination model based on user and information attributes.…”

Section: Introductionmentioning

confidence: 99%

SCWIR: A rumor propagation model considering user waiting behavior

Wu,

Yan,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

The rapid development of the Internet has brought convenience to people in accessing information, but it has also exacerbated the spread of rumors. Therefore, it becomes crucial to study the propagation patterns of rumors and devise corresponding suppression strategies. In the study of rumor propagation, the existing rumor propagation model studies have not fully considered the neutral wait-and-see population, nor have they taken into account the possibility of users waking up midway after believing in the rumor to wait and see for a second time. To address the above shortcomings, this paper established the SCWIR (Susceptible, Commented, Waited, Infected, Recovered) rumor propagation model, which introduced the Commenter (Commented, C) and the Waited (Waited, W) to better model users' positions of commenting and neutral waiting in rumor events. Based on the proposed model, this paper investigated the transformation process of five types of users, namely susceptible users, commenters, waiting users, spreaders, and recovery users, under the influence of government intervention, higher education coverage rate, and information timeliness; and conducted simulation experiments to verify the validity of the proposed model. The research results show that the model proposed in this paper can better reflect the possible behaviors of users such as commenting on rumors, neutral waiting, and spreading rumors during the rumor-spreading process. It achieves good performance in predicting the peak periods of rumor outbreaks, the peak value of outbreaks, the lifecycle of rumors, and the changing proportions of various members over time.

show abstract

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

Yan,

Yu,

et al. 2024

Nonlinear Dyn

View full text Add to dashboard Cite

UHIR: An effective information dissemination model of online social hypernetworks based on user and information attributes

Cited by 7 publications

References 34 publications

A Survey of Information Dissemination Model, Datasets, and Insight

A Survey of Information Dissemination Model, Datasets, and Insight

SCWIR: A rumor propagation model considering user waiting behavior

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

Contact Info

Product

Resources

About