[Objective] In order to quickly find out the most influential nodes in the network, this paper proposes a method to maximizing influence by overlapping communities structure, IMtoc.

[Methods] In this algorithm, the whole social network is divided into several overlapping communities, and the candidate seed set is selected by synthesizing the nodes with the largest feature vector centrality and overlapping nodes, and then the optimal seed node is found in the candidate set by greedy algorithm.

[Results] The results show that for the large social network Git_web_ml dataset, the running time of the IMtoc algorithm is about 110% and 65% faster than the CELF and IMRank algorithms

[Limitations] There is a large overlap between influential nodes and overlapping nodes, resulting in insufficient representation of some nodes.

[Conclusions] Compared with the existing methods, the IMtoc algorithm has certain advantages, which can achieve a better balance between the scope of influence and the running time.