The influence maximization (IM) is a key algorithm problem in information dissemination research. it aims to select a set of K users (also called seed sets) from a network and maximize the number of affected users (influence spread) through a specific information dissemination model. However, despite its huge application potential, with the advent of the era of big data, all kinds of networks tend to be complicated, and there is relatively little research on influence maximization of multilayer networks in complex networks, because in these networks, nodes are different types. On the other hand, most of the existing research on influence maximization relies on greedy algorithms and can only obtain a single solution. With that in mind, we focus on the influence maximization problem of multilayer networks in complex networks. specifically, we first define some novel concepts about the process of information dissemination in multilayer networks; then, we construct the influence maximization problem in multilayer networks into a multi-objective optimization problem. Finally, we do a lot of experiments on the real datasets, and the results show that the algorithm in this paper has a large competitive advantage in the influence spread and running time compared with the existing influence maximization algorithm.

Qipeng Lu, Pengfei Ding. Research on Influence Maximization Method for Complex Network. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 3: 53-69. https://doi.org/10.25236/AJCIS.2020.030307.

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