Trust based latency aware influence maximization in social networks

Mohamadi-Baghmolaei, Rezvan; Mozafari, Niloofar; Hamzeh, Ali

doi:10.1016/j.engappai.2015.02.007

Cited by 35 publications

(14 citation statements)

References 23 publications

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“…Mohamadi-Baghmolaei et al [31] introduce a Trust-based Latency aware Influence Maximization (TLIM) model in which time and trust are used to select influential seed nodes. On the other hand, Ali et al [32] propose a learning model for the time-bounded influence maximization model.…”

Section: A Influence Maximization Modelsmentioning

confidence: 99%

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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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Section: A Influence Maximization Modelsmentioning

confidence: 99%

“…We use the Monte Carlo (MC) simulation for the performance analysis [9]. We set infection and recovery probability by (30) and (31), respectively. Further, we compute convergence time slots T = 11 by equation (29), with γ = 0.94 [57].…”

Section: B Simulation Setupmentioning

confidence: 99%

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

Talukder

Hong

2020

IEEE Access

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“…Other works tried to consider other parameters to improve the quality of the selected seed nodes. Among these parameters we find the topic [23,24], trust [25,26], time [27], etc.…”

Section: Diffusion Model Based Influence Maximizationmentioning

confidence: 99%

Two evidential data based models for influence maximization in Twitter

Jendoubi

Martin

Liétard

et al. 2017

Knowledge-Based Systems

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Influence maximization is the problem of selecting a set of influential users in the social network. Those users could adopt the product and trigger a large cascade of adoptions through the "word of mouth" effect. In this paper, we propose two evidential influence maximization models for Twitter social network. The proposed approach uses the theory of belief functions to estimate users influence. Furthermore, the proposed influence estimation measure fuses many influence aspects in Twitter, like the importance of the user in the network structure and the popularity of user's tweets (messages). In our experiments, we compare the proposed solutions to existing ones and we show the performance of our models.

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“…Updating component of IDNDF is [12,29] inspired by PSO which is a population-based stochastic optimisation technique. Equations (5) and (6) formulate how information updates in each node of the network…”

Section: Information Updatingmentioning

confidence: 99%

“…It concerns the information propagation and opinion sharing between members of a network. According to Guille et al [1], different researches in the field of information diffusion have been categorised into three general branches: 'Detecting Interesting Topics' [2][3][4], 'Identifying Influential Spreaders' [5][6][7][8] and 'Modelling Diffusion Processes' [9][10][11][12][13]. Our main focus in this study is the latest branch, knowing as 'information diffusion modelling'.…”

Section: Introductionmentioning

confidence: 99%

Modelling information diffusion based on non-dominated friends in social networks

Mozafari

Hamzeh

Hashemi

2016

Journal of Information Science

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In recent years, social networks have played a strong role in diffusing information among people all around the globe. Therefore, the ability to analyse the diffusion pattern is essential. A diffusion model can identify the information dissemination pattern in a social network. One of the most important components of a diffusion model is information perception which determines the source each node receives its information from. Previous studies have assumed information perception to be just based on a single factor, that is, each individual receives information from their friend with the highest amount of information, whereas in reality, there exist other factors, such as trust, that affect the decision of people for selecting the friend who would supply information. These factors might be in conflict with each other, and modelling diffusion process with respect to a single factor can give rise to unacceptable results with respect to the other factors. In this article, we propose a novel information diffusion model based on non-dominated friends (IDNDF). Non-dominated friends are a set of friends of a node for whom there is no friend better than them in the set based on all considered factors, considering different factors simultaneously significantly enhance the proposed information diffusion model. Moreover, our model gives a chance to all non-dominated friends to be selected. Also, IDNDF allows having partial knowledge by each node of the social network. Finally, IDNDF is applicable to different types of data, including well-known real social networks like Epinions, WikiPedia, Advogato and so on. Extensive experiments are performed to assess the performance of the proposed model. The results show the efficiency of the IDNDF in diffusion of information in varieties of social networks.

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Trust based latency aware influence maximization in social networks

Cited by 35 publications

References 23 publications

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

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

Two evidential data based models for influence maximization in Twitter

Modelling information diffusion based on non-dominated friends in social networks

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