Graph mining is an important technique that used in many applications such as predicting and understanding behaviors and information dissemination within networks. One crucial aspect of graph mining is the identification and ranking of influential nodes, which has applications in various fields including marketing, social communications, and disease control. However, existing models and methods come with high computational complexity and may not accurately distinguish and identify influential nodes. This paper develops a method based on the k-shell index and degree centrality of nodes and their neighbors. Comparisons to previous works, such as Degree and Neighborhood information Centrality (DNC) and Neighborhood and Path Information Centrality (NPIC), are conducted. The evaluations, which include the correctness with Kendall's Tau, resolution with monotonicity index, correlation plots, and time complexity, demonstrate its superior results.