Influence maximization algorithm based on community structure

In recent years, how to identify the most influential nodes has become the forefront of network science. Considering the influence of community structure and neighbor nodes within the second order on node influence diffusion, this paper proposes an influence maximization algorithm based on community structure (IMCS). Firstly, the CPM algorithm is used to detect the community of the network to obtain the community structure of network. Then, select the nodes belonging to multiple communities in the community and some potential nodes in each community to form a candidate node set. Finally, use the improved Prob-Degree algorithm to screen all seed nodes. Experimental data show that compared with Prob-degree, CoFIM and DD, the algorithm proposed in this paper has relatively good overall performance in Oregon network, and there are seed node intervals with relatively good performance in other networks too.