Recent Advances in Information Diffusion and Influence Maximization of Complex Social Networks

Assessing information diffusion models for influence maximization in signed social networks

Expert Systems with Applications ◽

10.1016/j.eswa.2018.07.064 ◽

2019 ◽

Vol 119 ◽

pp. 476-490 ◽

Cited By ~ 12

Author(s):

Maryam Hosseini-Pozveh ◽

Kamran Zamanifar ◽

Ahmad Reza Naghsh-Nilchi

Keyword(s):

Social Networks ◽

Information Diffusion ◽

Influence Maximization ◽

Diffusion Models ◽

Signed Social Networks

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Influence Maximization Based on Backward Reasoning in Online Social Networks

Mathematics ◽

10.3390/math9243189 ◽

2021 ◽

Vol 9 (24) ◽

pp. 3189

Author(s):

Lin Zhang ◽

Kan Li

Keyword(s):

Social Networks ◽

Social Network ◽

Online Social Networks ◽

Information Diffusion ◽

Online Social Network ◽

Rapid Development ◽

Influence Maximization ◽

Research Issues ◽

Backward Reasoning ◽

New Framework

Along with the rapid development of information technology, online social networks have become more and more popular, which has greatly changed the way of information diffusion. Influence maximization is one of the hot research issues in online social network analysis. It refers to mining the most influential top-K nodes from an online social network to maximize the final propagation of influence in the network. The existing studies have shown that the greedy algorithms can obtain a highly accurate result, but its calculation is time-consuming. Although heuristic algorithms can improve efficiency, it is at the expense of accuracy. To balance the contradiction between calculation accuracy and efficiency, we propose a new framework based on backward reasoning called Influence Maximization Based on Backward Reasoning. This new framework uses the maximum influence area in the network to reversely infer the most likely seed nodes, which is based on maximum likelihood estimation. The scheme we adopted demonstrates four strengths. First, it achieves a balance between the accuracy of the result and efficiency. Second, it defines the influence cardinality of the node based on the information diffusion process and the network topology structure, which guarantees the accuracy of the algorithm. Third, the calculation method based on message-passing greatly reduces the computational complexity. More importantly, we applied the proposed framework to different types of real online social network datasets and conducted a series of experiments with different specifications and settings to verify the advantages of the algorithm. The results of the experiments are very promising.

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