Influence maximization algorithm based on Gaussian propagation model

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. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). In recent years, social networks have become an indispensable part of modern social life. People communicate and collaborate on various social networks, with a large amount of data being generated during the communication process. This dependence on social networks has prompted extensive analysis toward finding solutions to the intricacy of influence maximization. Influence maximization is a fundamental and critical issue. In practical applications such as the spread of news, outbreak of diseases, viral marketing, and rumor control, influence maximization techniques are required. The initial solution of influence maximization is based on the framework of the greedy algorithm [10] which requires con

Automated summary

The paper proposes an influence maximization algorithm based on the Gaussian propagation model, which improves effectiveness and efficiency through multidimensional space modeling and parameter control.