A node filtering approach for Influence Maximization problem in Independent Cascade model

Identify influential nodes is essential to the immunization and propagation process on complex networks that this node selects based on the influence maximization problem. However, most of the previous strategies face many challenges, such as accuracy and efficiency. To solve these challenges, in this paper, we propose a novel influence maximization algorithm, named NFIM (Node filtering in influence maximization), which is based on deleting a maximal independent set and creating a subgraph. First, the search space for selecting seed nodes is reduced in the NFIM algorithm Then, seed nodes are selected by examining independent paths and clustering coefficient. Experiment results present that the NFIM algorithm performs better than PHG, LIR, CI, and ProbDegree on influence spread and faster than PHG, LIR, and CI.