Intelligent Filtering of Graph Shells in the Problem of Influence Maximization Based on the Independent Cascade Model

In social networks, the problem of influence maximization seeks for a solution to find individuals or nodes in different communities so that they can diffuse information influence among a wide range of other nodes. The proposed algorithms for influence maximization problem have many drawbacks. For example, the computational overhead is very high and also the seed nodes is not selected optimally. For this reason, the influence does not spread totally in the social network.for solving the problem, This paper provides the SFIM algorithm and uses the idea of layering community nodes and identifying valuable layers to limit the search space. The operation is continued only on nodes of valuable layers, which significantly reduces the algorithm's runtime. Then, the best set of influential nodes with the highest accuracy is found by considering the main criteria of centrality topology such as harmonic and degree. Accuracy in selecting a node is one of the most important needs of the problem that is best answered. Moreover, different experiments and datasets indicate that this algorithm can provide the best efficiency required to solve the problem compared to other algorithms.