Influence maximization algorithm based on Gaussian propagation model

作     者：Li, WeiMin Li, Zheng Luvembe, Alex Munyole Yang, Chao 

作者机构：Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Lixin Univ Accounting & Finance Shanghai 201209 Peoples R China 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2021年第568卷

页      面：386-402页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key RAMP D Program of China [2017YFE0117500] Natural Science Foundation of China 

主　　题：Social network Influence maximization Gaussian propagation model Greedy algorithm 

摘      要：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://***/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 maximiza