An Efficient Algorithm for Influence Maximization in Signed Social Networks

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Namiranian Mozhgan; MEYBODI MOHAMMAD REZA

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Seminar Paper

Paper Information

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

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Title

An Efficient Algorithm for Influence Maximization in Signed Social Networks

Author(s)

Namiranian Mozhgan | MEYBODI MOHAMMAD REZA

Keywords

Social Network

Influence Maximization

Negative Opinions

Marginal Increase

Independent Cascade Model

Label Propagation

Abstract

Nowadays, much attention has been devoted to the issues of Social Networks and social influence. Social influence examines the user's behavioral changes under the influence of their neighbors. The issue of Influence Maximization is to find a subset of influential nodes that can maximize propagation in the network. The selection of people is very important and is the major aim of the studies. Hence, the current study aims to investigate the maximization of influence in signed Social Networks since in the psychology of society, Negative Opinions are superior to positive ones. The criteria considered for measuring influence and methods to increase it by identifying influential people are examined. The proposed solution of this paper is based on the Label Propagation algorithm. The algorithms used for maximizing influence in signed Social Networks namely a greedy algorithm and an innovative algorithm are outlined in the second section. To implement the algorithms and simulate the transfer of users' opinions in the graph network, the independent cascade propagation model is used. The proposed algorithm shows better performance and results compared to other algorithms and has less computational overhead since it finds primary nodes by detecting dense parts and not randomly. The significant novelty of the paper lies in the heart of the accuracy and authenticity of the proposed model in maximizing influence in signed Social Networks.

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APA: Copy

Namiranian, Mozhgan, & MEYBODI, MOHAMMAD REZA. (). . . SID. https://sid.ir/paper/1046851/en

Vancouver: Copy

Namiranian Mozhgan, MEYBODI MOHAMMAD REZA. . . Available from: https://sid.ir/paper/1046851/en

IEEE: Copy

Mozhgan Namiranian, and MOHAMMAD REZA MEYBODI, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046851/en

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