2023
DOI: 10.21928/uhdjst.v7n2y2023.pp32-39
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Link Prediction in Dynamic Networks Based on the Selection of Similarity Criteria and Machine Learning

Karwan Mohammed HamaKarim

Abstract: The study’s findings showed that link prediction utilizing the similarity learning model in dynamic networks (LSDN) performed better than other learning techniques including neural network learning and decision tree learning in terms of the three criteria of accuracy, coverage, and efficiency., Compared to the random forest approach, the LSDN learning algorithm’s link prediction accuracy increased from 97% to 99%. The proposed method’s use of oversampling, which improved link prediction accuracy, was the cause… Show more

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