Information Diffusion and Opinion Leader Mathematical Modeling Based on Microblog

Liu, Xiaoyang; Liu, Chao

doi:10.1109/access.2018.2849722

Cited by 30 publications

(16 citation statements)

References 20 publications

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“…Meanwhile, more edges are added in sparse networks, leading to a wider range of information diffusion. Empirically, the best reconstruction accuracy is obtained when the average node degree is in the interval (2,6), which corresponds to the average node degree of many real networks such as the Power Grid network (the average node degree is 2.67) and the HepTh network (the average node degree is 5.26), etc. Thus, experiment results on real networks are better than experiment results on many synthetic networks.…”

Section: Discussionmentioning

confidence: 99%

“…The study of information diffusion in social networks has received considerable interest due to its wide applications in many problems [1], [2]. A number of studies on information diffusion focus on investigating the effect of network structure on spreading cascades because cascading processes are ubiquitous in networks, where a small set of nodes can affect a much larger set with intricate individual interaction patterns [3]- [7].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Network Structure on Information Diffusion Reconstruction

Chu

2019

IEEE Access

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This paper considers the effect of network structure on the reconstruction of information diffusion in a network. We employ the independent cascade model and a generalized independent cascade model to describe the network diffusing process with a single influence attempt and multiple influence attempts occurred between a pair of nodes, respectively. The diffusion reconstruction is formulated as a maximization likelihood problem. Based on this, we investigate the effect of the node number and the edge density of a network on the performance of diffusion reconstruction with numerical experiments on synthetic and real networks. The results show that reconstruction accuracies are inversely related to the node number and nonlinearly depends on the edge density. We also discuss the effect of the number of influence attempts in diffusion on the reconstruction accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of Network Structure on Information Diffusion Reconstruction

Chu

2019

IEEE Access

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“…Liu and Liu [35] develop an opinion leader-based emergency public information diffusion model for microblog. Ye et al [36] use the community-based approach to identify influential users in social networks.…”

Section: A Influence Maximization Modelsmentioning

confidence: 99%

CM2D: Cost Minimization Under the Competitive Market in Dynamic Social Networks

Talukder

Hong

2020

IEEE Access

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The Reverse Influence Maximization (RIM) model deals with the viral marketing cost minimization in social networks. On the other hand, the Influence maximization (IM) technique finds the small number of influential users that maximize the viral marketing profit. Here, the profit is defined by the maximum number of nodes that can be influenced by seed users when they are initially activated. On the other hand, the cost is measured by the minimum number of nodes required to activate all the seed nodes. However, most of the existing studies focus on profit maximization without considering the seeding cost. Moreover, the most profitable node may not always be a cost-effective one. Thus, in this research, we introduce an epidemic Cost Minimization model under the Competitive Market in Dynamic social networks (CMCMD, i.e., CM2D). In the CM2D model, we modify the Susceptible-Infected-Recovered (SIR) technique as the Reverse SIR (RSIR) model to employ in the node activation process. The proposed CM2D model also uses the greedy Set Cover approximation technique to optimize the seeding cost. Moreover, the CM2D model tackles the challenging issues of the RIM problem more efficiently than the existing models. Finally, we simulate our model using datasets of two synthesized and four real datasets, and the results show that the CM2D model outperforms the existing models.

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“…In recent years, with the development of the Internet, some online social platforms, such as Twitter and Weibo, have been created. They not only provided a new form for individuals to post and exchange information, but also led to the formation of a new form of spammers, whose posting information is no longer limited to emails, but extending to various forms, such as microblogs and comments [19], [20]. So far, the word ''spammer'' is not restricted in the mail service system but has much broader meanings.…”

Section: Related Workmentioning

confidence: 99%

An Internet Water Army Detection Supernetwork Model

et al. 2019

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The emergence of Internet water armies has strongly affected the information quality of online communication platforms, thus disrupting the order of the Internet. Accurate detection of Internet water armies is therefore of great significance. Based on the supernetwork theory, a new Internet water army detection model is proposed in this paper, in which a supernetwork with four layers is established, including social subnetwork, information subnetwork, psychological subnetwork, and negative keyword subnetwork. Then, personal information of users, dissemination process of information, the transformation process of different psychologies, the similarity between different keywords, and the connections between different subnetworks are considered in the model. Thus, nine composite indexes are proposed, the majority of which are used for the first time in detecting Internet water armies. A dataset selected from the largest online communication platform in China, the Weibo website, is used to test the performance of the model. Four existing water army detection models introduced in previous studies are used to provide a comparison analysis. The results show that our proposed model has better performance in terms of accuracy and stability than the other four existing models, which thanks to the employment of the supernetwork theory. We believe that our proposed model could be helpful for information researchers to further understand the complex nature of Internet water armies, as well as for the government to better manage the Internet.

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Information Diffusion and Opinion Leader Mathematical Modeling Based on Microblog

Cited by 30 publications

References 20 publications

Effects of Network Structure on Information Diffusion Reconstruction

Effects of Network Structure on Information Diffusion Reconstruction

CM2D: Cost Minimization Under the Competitive Market in Dynamic Social Networks

An Internet Water Army Detection Supernetwork Model

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