Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks

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Khosravi Ehsan; Yazdanifar Fatemeh; Salimi Nikoo; Ahmadi Beni Hamid

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Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks

Author(s)

Khosravi Ehsan | Yazdanifar Fatemeh | Salimi Nikoo | Ahmadi Beni Hamid

Keywords

Influence Maximization

Diffusion Model

Seed Nodes

Reachability

Social Networks

Abstract

With the development of science and technology through Social Networks, the process of diffusion has become a controversial subject. In Social Networks, the process of diffusion is transmitted from one individual to another. The Influence Maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of Influence Maximization, the real-world factors which affect the influence probability in Diffusion Models are ignored for example, the influence probability is determined randomly. This problem causes Influence Maximization algorithms based on Diffusion Models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local Reachability. In addition, Due to privacy in Social Networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in Social Networks, the influence probability is calculated in the new DBT Diffusion Model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time.

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APA: Copy

Khosravi, Ehsan, Yazdanifar, Fatemeh, Salimi, Nikoo, & Ahmadi Beni, Hamid. (). . . SID. https://sid.ir/paper/1046873/en

Vancouver: Copy

Khosravi Ehsan, Yazdanifar Fatemeh, Salimi Nikoo, Ahmadi Beni Hamid. . . Available from: https://sid.ir/paper/1046873/en

IEEE: Copy

Ehsan Khosravi, Fatemeh Yazdanifar, Nikoo Salimi, and Hamid Ahmadi Beni, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046873/en

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