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With the emergence of social networks, human relationships on the internet have become a new form. Social networks are not only a communication tool for users, but can also be a basis for marketing and advertising products of different companies. Studying the impact of maximum penetration has attracted many researchers in recent years due to the benefits of viral marketing. Given a social network, the goal is to find a subset of K individuals as influential nodes that can generate maximum cascading influence through the network under a predefined diffusion model. The first research in this field did not work for large networks. After this effort, different methods were presented to maximize influence, among them, methods based on communities were proposed. Algorithms for maximizing community influence often use the influence of a node in its own community to approximate its influence in the entire network, so they can perform better. One of these community-based algorithms is the COFIM algorithm. In this paper, the efficiency of the COFIM algorithm, which is a community-based influence maximization method, is improved by distributing seed nodes through the community structure. The results of the proposed algorithm have been tested on six different data sets and then compared with the basic methods. The test results show the efficiency of the proposed method.
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