Maximum diffusion of news in social media with the approach of reducing the search space

Identification of nodes that spread influence is an important aspect of social network analysis. These nodes are used for maximizing influence. Influence maximization (INMAXI) is basically NP-Hard. This issue, with large-scale data, faces many challenges such as accuracy and efficiency. This paper offers a new approach in this area, named RSP (Reducing search space in INMAXI). The RSP algorithm uses centralities and shells of social networks for selecting super-spreaders. The nodes in the shortest path are of great importance in the RSP algorithm. Unlike other algorithms, this algorithm does not ignore low-degree nodes. Experiments indicate that the RSP algorithm works better than RNR, MCGN, LMP, and LIR on influence spread and maintains the quality of the results in every way.