Volume 18, Issue 2 (10-2021)
JSDP 2021, 18(2): 57-74 |
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Abbaspour orangi M, Hashemi golpayegani A. Identifying Influential Nodes to Diffuse the Trusting Behavior in Social Networks. JSDP 2021; 18 (2) :57-74
URL: http://jsdp.rcisp.ac.ir/article-1-967-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-967-en.html
Department of Computer Engineering and Information Technology, Amirkabir University of Technology
Abstract: (1762 Views)
Trust is one of the most important cornerstones in social networks' discussions. mostly the way that users of these networks trust each other are considered identical, while these users can have different approaches and considerations in trusting others. Meanwhile, users can impress each other and change their trusting patterns in other users. As a result, the mechanism and manner of impressing opinion trust behavior and conditions of behavioral modes changing have a place of importance to be considered. The question is that, how we can consider different behavior of users and their impression in trusting others? In the first step, the main purpose of this paper is to spotlight social networks' different user behavior in trusting other users. For this purpose, the three most important behavioral modes in users trust are considered. In each of these modes, behavioral and functional characteristics of users are the basis of calculating trust, which is based on mental beliefs of them. These modes are named as optimistic, moderate, and pessimistic trusting modes. In optimistic mode, we suppose that users think positively and consider low level of activities and signs in trusting others. Here, negative interactions have little impact on users mind. In moderate mode, we suppose that users are not as optimistic as mode A and consider all the interactions and signs when they want to trust others. Here, any negative action can destroy the trust of users and has a greater impact on users. Finally, in pessimistic mode, we suppose that users are pessimistic and hardly trust someone. In this mode, the interactions that happened more recently have more value than those happened in the past.
In the next step, the purpose and innovation of this paper is the way that the trust behavior of users spreads. Three different scenarios are considered for the impressing and spreading of nodes behavior, purposely. In each scenario, different states for users and different purposes for diffusion are defined. Next, it is followed by maximizing of impression and finding more impressive agents in diffusing trust behavior through social networks. For this purpose, it's focused on the structure of users social networks, and the most impressive ones are determined through different diffusion scenarios. The findings of this article appear a significant discrepancy in the amount of trust in each of the different behavioral modes, which is more acceptable in the real world. Analyzing test results leads us to the fact that in the presented model, choosing the start node from each community with 48.14 percent in behavior improvement and diffusion speed and the nodes with the highest degree with 37.03 percent in behavior changing has much more reasonable results than usual models.
In the next step, the purpose and innovation of this paper is the way that the trust behavior of users spreads. Three different scenarios are considered for the impressing and spreading of nodes behavior, purposely. In each scenario, different states for users and different purposes for diffusion are defined. Next, it is followed by maximizing of impression and finding more impressive agents in diffusing trust behavior through social networks. For this purpose, it's focused on the structure of users social networks, and the most impressive ones are determined through different diffusion scenarios. The findings of this article appear a significant discrepancy in the amount of trust in each of the different behavioral modes, which is more acceptable in the real world. Analyzing test results leads us to the fact that in the presented model, choosing the start node from each community with 48.14 percent in behavior improvement and diffusion speed and the nodes with the highest degree with 37.03 percent in behavior changing has much more reasonable results than usual models.
Keywords: Trusting behavior, Trust diffusion, Influential nodes, Social networks, Social network analysis
Type of Study: Research |
Subject:
Paper
Received: 2019/02/4 | Accepted: 2021/01/10 | Published: 2021/10/8 | ePublished: 2021/10/8
Received: 2019/02/4 | Accepted: 2021/01/10 | Published: 2021/10/8 | ePublished: 2021/10/8
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