Influence maximization algorithm based on Gaussian propagation model

•A Gaussian propagation model is explored to improve Influence Maximization.•Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions.•An improved CELF algorithm to accelerate the efficiency of the greedy algorithm. The influence of each entity in a network is a crucia...

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Bibliographic Details
Published in:Information sciences Vol. 568; pp. 386 - 402
Main Authors: Li, WeiMin, Li, Zheng, Luvembe, Alex Munyole, Yang, Chao
Format: Journal Article
Language:English
Published: Elsevier Inc 01.08.2021
Subjects:
Gaussian propagation model
Greedy algorithm
Influence maximization
Social network
Greedy algorithm
Influence maximization
Social network
Gaussian propagation model
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:•A Gaussian propagation model is explored to improve Influence Maximization.•Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions.•An improved CELF algorithm to accelerate the efficiency of the greedy algorithm. The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space’s circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2021.04.061