Influential node detection in networks is vital in diverse applications, particularly with the online social networks (OSNs). Measures of centrality, traditional ones, fail to sufficiently describe the influence of nodes on complex and multilayered networks. This paper presents a novel hybrid method that combines traditional topological centrality metrics with machine learning to be able to detect influential nodes. The proposed approach applies degree, betweenness, closeness, eigenvector centrality, PageRank, and clustering coefficients as the characteristics of a Hybrid Random Forest classifier, which is improved with Gradient Boosting Decision Trees (RF-GBDT). The model is tested using the simulation based on the dynamics of interaction between influencers in a network. The findings show that the RF-GBDT approach is much more effective than conventional methods as it has an accuracy of 96.7%. The hybrid approach is better in detecting influential people, which is essential in maximizing brand marketing in the social media. The results indicate that topological characteristics and machine learning models can be used together to provide more accuracy in the detection of key players through OSN analysis. The implications that this methodology would have on targeted marketing, social media analytics, and online community development may be significant. Further investigation opportunities exist to develop the model further on the scalability in generalized network environments and to apply the model to larger social and professional network environments.
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