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Given that real\u2010world scale\u2010free networks are dynamic and time based, a temporal\u2010scale\u2010free network (TSF network) is proposed in this paper. To predict the emergence of hubs, proposed a temporal graph convolutional neural network (T\u2010GCN) that integrates graph convolutional networks (GCNs) for spatial feature extraction and long short\u2010term memory (LSTM) networks for modeling temporal dynamics. Our framework effectively learns both the structural evolution and dynamic node interactions in scale\u2010free networks, allowing accurate prediction of hub emergence. The proposed model is trained on synthetic and real\u2010world datasets, demonstrating superior predictive accuracy compared to traditional methods. 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