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.

中文翻译：

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EC-BED-NETS：一种新的深度学习框架，用于识别多面和社交网络中的主导节点

识别多方面和社交网络中的有影响力的节点成为这个蓬勃发展的数字世界中最重要的研究之一。提出了许多策略来根据节点在网络中的拓扑信息来确定节点的优势。传统上，中心性测量直接用于网络的拓扑结构，这些测量考虑了与结构和功能重要性相关的不同特征。节点的功能重要性之间的非线性联系，这使得研究如此复杂且难以使用传统的中心性度量来检测。受长短期记忆（LSTM）惊人的执行结构的启发，本文提出了新的混合增强集成 LSTM 框架来解决上述问题。该框架采用增强中心性方法构建能够反映其网络中节点功能和结构位置的不同特征向量，然后根据测量值对节点进行分类，最后使用本文提出的增强型深度学习框架进行分类并对有影响的节点进行排名。从广泛的实验来看，所提出的框架显示出 95.5% 的最佳分类准确率，并且优于其他机器和深度学习模型，甚至优于传统的中心性测量。最后使用提出的增强深度学习框架对有影响的节点进行分类和排名。从广泛的实验来看，所提出的框架显示出 95.5% 的最佳分类准确率，并且优于其他机器和深度学习模型，甚至优于传统的中心性测量。最后使用提出的增强深度学习框架对有影响的节点进行分类和排名。从广泛的实验来看，所提出的框架显示出 95.5% 的最佳分类准确率，并且优于其他机器和深度学习模型，甚至优于传统的中心性测量。