Study On Coupled Social Network Public Opinion Communication Based On Improved SEIR

Jiang, Changbin; Zhang, Yaoyuan; Wang, Hu; Zhou, Yawen; Zou, Yueqi

doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00225

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…The propagation law of the public opinion coupled double-layer network is studied [31] Focuses on coupling mechanism of online public opinion events [32] Research on the spread speed of public opinion [33] Rumors spread…”

Section: Public Opinion Monitoringmentioning

confidence: 99%

Project Group Program Generation and Decision Making Method Integrating Coupling Network and Hesitant Fuzzy

Qian,

Dou,

Gong

et al. 2023

Mathematics

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Modern urban construction relies on a large number of projects. Project groups are an effective way to manage a large number of projects. In view of the current lack of scientific methods for constructing and evaluating project group programs, which are mainly based on subjective experience, this article proposes a scientific method for project group program generation and decision-making. The method proposed in this article applies a multi-layer coupling network to the modeling of project groups and divides projects into planning projects and execution projects to form a heterogeneous coupling network. Then, starting from the principle of project information dissemination, the evaluation indicators of the project group program were defined, and finally, the hesitant fuzzy decision-making method was used to assist in decision making. This article can provide a new method for project group construction and management, and provide strong support for the construction of smart cities and digital governments.

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Section: Public Opinion Monitoringmentioning

confidence: 99%

Project Group Program Generation and Decision Making Method Integrating Coupling Network and Hesitant Fuzzy

Qian,

Dou,

Gong

et al. 2023

Mathematics

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“…Most existing research on the spread of epidemics focuses on practical applications, e.g., predictions of scale or effects of control measures, and little focuses on the pattern recognition in epidemic dispersal scenarios. Since the mechanisms of public opinion transmission and epidemic dispersal are similar [5][6][7], with the former being a hot topic of online community influence analysis, it is possible to view an epidemic as an influence between cities and to conclude an influence propagation process from epidemic dispersal scenarios. Therefore, the authors of this study intend to take advantage of the findings of spatial co-location pattern mining fields and explore meaningful high influencing patterns in an influence propagation process.…”

Section: Introductionmentioning

confidence: 99%

High Influencing Pattern Discovery over Time Series Data

Fang

Wang

et al. 2021

IJGI

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A spatial co-location pattern denotes a subset of spatial features whose instances frequently appear nearby. High influence co-location pattern mining is used to find co-location patterns with high influence in specific aspects. Studies of such pattern mining usually rely on spatial distance for measuring nearness between instances, a method that cannot be applied to an influence propagation process concluded from epidemic dispersal scenarios. To discover meaningful patterns by using fruitful results in this field, we extend existing approaches and propose a mining framework. We first defined a new concept of proximity to depict semantic nearness between instances of distinct features, thus applying a star-shaped materialized model to mine influencing patterns. Then, we designed attribute descriptors to perceive attributes of instances and edges from time series data, and we calculated the attribute weights via an analytic hierarchy process, thereby computing the influence between instances and the influence of features in influencing patterns. Next, we constructed influencing metrics and set a threshold to discover high influencing patterns. Since the metrics do not satisfy the downward closure property, we propose two improved algorithms to boost efficiency. Extensive experiments conducted on real and synthetic datasets verified the effectiveness, efficiency, and scalability of our method.

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Model Construction and Empirical Research on Cross-Platform Propagation of Disinformation

Chen,

Jiang,

2024

Lecture Notes in Computer Science

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Study On Coupled Social Network Public Opinion Communication Based On Improved SEIR

Cited by 4 publications

References 10 publications

Project Group Program Generation and Decision Making Method Integrating Coupling Network and Hesitant Fuzzy

Project Group Program Generation and Decision Making Method Integrating Coupling Network and Hesitant Fuzzy

High Influencing Pattern Discovery over Time Series Data

Model Construction and Empirical Research on Cross-Platform Propagation of Disinformation

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