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Abstract
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In this paper, an innovative architecture based on deep neural networks is presented. Initially, node and layer
features are extracted as feature vectors. Each vector is then passed through a deep multilayer perceptron
(MLP) network for enrichment. Using the Hadamard product, these vectors are multiplied element-wise to
form a matrix. In the next step, to analyze feature interactions, this matrix is fed into a series of Transformer
encoders arranged sequentially. Finally, an MLP network is used as a regression model to predict the influence
power of the nodes.
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