The connection between users in social networks can be maintained for a certain period of time, and the static network structure formed provides the basic conditions for various kinds of research, especially for discovering customer groups that can generate great influence, which is important for product promotion, epidemic prevention and control, and public opinion supervision, etc. However, the computational process of influence maximization ignores the timeliness of interaction behaviors among users, the screened target users cannot diffuse information well, and the time complexity of relying on greedy rules to handle the influence maximization problem is high. Therefore, this paper analyzes the influence of the interaction between nodes in dynamic social networks on information dissemination, extends the classical independent cascade model to a dynamic social network dissemination model based on effective links, and proposes a two-stage influence maximization solution algorithm (Outdegree Effective Link—OEL) based on node degree and effective links to enhance the efficiency of problem solving. In order to verify the effectiveness of the algorithm, five typical influence maximization methods are compared and analyzed on four real data sets. The results show that the OEL algorithm has good performance in propagation range and running time.

The influence maximization problem is designed to seek a set of nodes in a social network so that the set has the maximum information propagation capacity on the network. In response to the inefficiency of existing greedy algorithms and the low accuracy of centrality-based heuristics, we propose an improved differential evolution algorithm (IDDE) based on the network discretization in this paper. The algorithm improves the variance rule of the differential evolution algorithm, takes the discrete number and discrete granularity of the remaining network after the removal of the target node as the index to evaluate the importance of the node, and proposes a fitness function based on the robustness of the network. The method embodies symmetry in two aspects. Firstly, the global connectivity among nodes in the network decreases as the number of target nodes removed in the social network increases. Secondly, the gain of global influence range gradually becomes smaller as the number of target nodes screened by the proposed method increases. We conducted comparison experiments on four real datasets of different sizes, and the results show that the IDDE algorithm outperforms the comparison algorithm.