Identification of influential nodes in multifaceted and social networks become one of the most significant researches in this booming digital world. Many strategies were proposed to determine the dominance of nodes based on their topographical information in the networks. Traditionally, centrality measurements were used directly on topographical structure of the networks and these measurements consider different characteristics related to structural and functional importance. The nonlinear link between the functional importance of the nodes, which makes the study so complicated and difficult to detect using traditional centrality measures. Inspired by the amazing execution structure of long short-term memory (LSTM), this article proposes the new hybrid boosted ensemble LSTM framework for solving the mentioned problem. This proposed framework adopts the enhanced centrality methods to construct the different feature vectors that can reflect the functional and structural location of the nodes in their networks, then categorizes the nodes in accordance with the measurements, and finally uses the proposed boosted deep learning framework to classify and rank the influential nodes. From the extensive experiments, the proposed framework has shown the best classification accuracy of 95.5% and it outperforms the other machine and deep learning models and even traditional centrality measurements.
Keywords: centrality; complex and social networks; deep learning model; influentiality; long short-term memory.