Information spreading on dynamic social networks

Liu, Chuang; Zhang, Zi-Ke

doi:10.1016/j.cnsns.2013.08.028

Cited by 147 publications

(63 citation statements)

References 48 publications

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“…Information diffusion is related to time, relationship strength, information content, social factors, network structure [25], etc. Researchers have made ongoing improvements based on classical models, developing new models such as SEIR (Suceptible Exposed Infected Removed) model [26], S-SEIR (Single layer-SEIR) [27], SCIR (Suceptible Contacted Infected Removed) model [28], irSIR (infection recovery SIR) model [29], FSIR (Fractional SIR) model [30] and ESIS (Emotional Suceptible Infected Suceptible) model [31].…”

Section: Epidemic Models In Social Networkmentioning

confidence: 99%

A Survey on Information Diffusion in Online Social Networks: Models and Methods

et al. 2017

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By now, personal life has been invaded by online social networks (OSNs) everywhere. They intend to move more and more offline lives to online social networks. Therefore, online social networks can reflect the structure of offline human society. A piece of information can be exchanged or diffused between individuals in social networks. From this diffusion process, lots of latent information can be mined. It can be used for market predicting, rumor controlling, and opinion monitoring among other things. However, the research of these applications depends on the diffusion models and methods. For this reason, we survey various information diffusion models from recent decades. From a research process view, we divide the diffusion models into two categories-explanatory models and predictive models-in which the former includes epidemics and influence models and the latter includes independent cascade, linear threshold, and game theory models. The purpose of this paper is to investigate the research methods and techniques, and compare them according to the above categories. The whole research structure of the information diffusion models based on our view is given. There is a discussion at the end of each section, detailing related models that are mentioned in the literature. We conclude that these two models are not independent, they always complement each other. Finally, the issues of the social networks research are discussed and summarized, and directions for future study are proposed.

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Section: Epidemic Models In Social Networkmentioning

confidence: 99%

A Survey on Information Diffusion in Online Social Networks: Models and Methods

et al. 2017

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“…Namely, one can imagine that newly launched Apps are initially installed by friends of the authors of the Apps due to social support [32]. Imagine that each App receives the same amount of social support.…”

Section: Resultsmentioning

confidence: 99%

Uncovering the popularity mechanisms for Facebook applications

Guo

Yang

et al. 2018

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• For different kinds of Facebook Apps and found that the recent and cumulative popularity play important roles.• We present a model to regenerate the growth of popularity for different App groups.• The recent popularity plays more important role in regenerating the popularity dynamics for more popular Apps.• The cumulative popularity plays more important role for unpopular Apps.Understanding the popularity dynamics of online application(App) is significant for the online social systems. In this paper, by dividing the Facebook Apps into different groups in terms of their popularities, we empirically investigate the popularity dynamics for different kinds of Facebook Apps. Then, taking into account the influence of cumulative and recent popularities on the user choice, we present a model to regenerate the growth of popularity for different App groups. The experimental results of 917 Facebook Apps show that as the popularities of Facebook Apps increase, the recent popularity plays more important role. Specifically, the recent popularity plays more important role in regenerating the popularity dynamics for more popular Apps, and the cumulative popularity plays more important role for unpopular Apps. We also conduct temporal analysis on the growth characteristic of individual App by comparing the increment at each time with the average of historical records. The results show that the growth of more popular App tends to fluctuate more greatly. Our work may shed some lights for deeply understanding the popularity mechanism for online applications.

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“…On the other hand, the real world networks is full of randomness and stochasticity, using stochastic models can gain more real benefits. Consequently, Some researchers have paid their attention to the stochastic Information Diffusion model [9,10]. We assumed that stochastic perturbations were of white noise type, consider that the rate of recovery from infection coefficient γ was subject to stochastic perturbations in model (1), i.e.…”

Section: S T S T I T S T R T I T S T I T I T R T I T R Tmentioning

confidence: 99%

Asymptotic Behavior of Global Positive Solution to an Information Diffusion Model with Random Perturbation in Social Network

Yuan¹,

Hu²

2018

dtcse

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Abstract. The paper explore an information diffusion models with random perturbation in social network. First, we show the models exit the unique global positive solution. By the construction of the Lyapunov function, we give the positive solution is stochastically asymptotically stable in the large around disease-free equilibrium, i.e. the conditions of the information diffusion will die out, investigate the stochastic asymptotic behavior of the positive solution around endemic equilibrium of the deterministic models, obtain the stochastic asymptotic stability condition, i.e. the conditions of the information diffusion will be persistent in social networks.

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Information spreading on dynamic social networks

Cited by 147 publications

References 48 publications

A Survey on Information Diffusion in Online Social Networks: Models and Methods

A Survey on Information Diffusion in Online Social Networks: Models and Methods

Uncovering the popularity mechanisms for Facebook applications

Asymptotic Behavior of Global Positive Solution to an Information Diffusion Model with Random Perturbation in Social Network

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