Node Immunization with Time-Sensitive Restrictions

Cui, Wen; Gong, Xiaoqing; Xu, Dan; Liu, Chen; Fang, Dingyi; Tang, Shaojie; Wu, Fan; Chen, Guihai

doi:10.3390/s16122141

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…Due to the complexity of actual social networks, we make the following assumptions on the designed model before conducting research [39][40][41][42]:…”

Section: Dr-scir Network Public Opinion Propagation Modelmentioning

confidence: 99%

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

Guo

Wang

et al. 2020

Symmetry

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The DR-SCIR network public opinion propagation model was employed to study the characters of S-state users stopping transmitting information for the first time and secondary transmission of immune users. The model takes into account symmetry and complexity such as direct immunization and social reinforcement effect, proposes the probability of direct immunity Psr and the probability of transform from the immune state to the hesitant state Prc, and divides public opinion information into positive public opinion and negative public opinion based on whether the public opinion information is confirmed. Simulation results show that, when direct immunity Psr = 0.5, the density of I-state nodes in the model decreased by 54.12% at the peak index; when the positive social reinforcement effect factor b = 10, the density of I-state nodes in the model increased by 16.67% at the peak index; and when the negative social reinforcement effect factor b = -10, the density of I-state nodes in the model decreased by 55.36% at the peak index. It shows that increasing the positive social reinforcement effect factor b can promote the spread of positive public opinion, reducing the negative social reinforcement effect factor b can control the spread of negative public opinion, and direct immunization can effectively suppress the spread of public opinion. This model can help us better analyze the rules of public opinion on social networks, so as to maintain a healthy and harmonious network and social environment.

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“…Due to the complexity of actual social networks, we make the following assumptions on the designed model before conducting research [39][40][41][42]:…”

Section: Dr-scir Network Public Opinion Propagation Modelmentioning

confidence: 99%

DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

Guo

Wang

et al. 2020

Symmetry

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“…On the other hand, the NI problem asks for a set of nodes that, if removed, would minimize the expected spread a diffusion emanating from some other source. These problems arise in social network analysis, viral marketing, and epidemiology [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Content-Based Network Influence Probabilities: Extraction and Application

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Karras

2019

2019 International Conference on Data Mining Workshops (ICDMW)

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The diffusion of information and influence in networks shapes ideas, habits, and behaviors. Several diffusion control methods have been proposed to harness, maximize, limit, or direct such diffusion processes in desirable ways. State-of-the-art algorithms for prescriptive fine-grained diffusion control rely on simple models, most prominently the Independent Cascade (IC) model, rather than on advanced machine learning approaches. The simplicity of such models can be an advantage. Yet, to exploit this advantage, one needs not only well-designed algorithms, but also a powerful model-training framework that yields wellinformed models. Unfortunately, much research effort has been devoted to algorithm design, while the development of techniques for informing the underlying model has been largely neglected. We propose a new content analysis workflow that derives realistic IC model parameters for diffusion control algorithms. We rely on a log of user text messages to derive a measure of similarity among those messages, and therefrom calculate the probability that one node influences another. We evaluate our model in terms of its predictive power and apply it to two representative diffusion control problems under the IC model. Our results showcase the capacity of our methods to make correct predictions, and provide the first, to our knowledge, study of diffusion control problems with a real-world probability model.

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