Neural Scale‐Free Network: A Novel Neural Network to Predict the Emergence of Hub Nodes in Complex Networks

The emergence of hubs in scale‐free networks plays a critical role in understanding dynamic complex networks such as social interactions, transportation networks, and biological processes. Given that real‐world scale‐free networks are dynamic and time based, a temporal‐scale‐free network (TSF network) is proposed in this paper. To predict the emergence of hubs, proposed a temporal graph convolutional neural network (T‐GCN) that integrates graph convolutional networks (GCNs) for spatial feature extraction and long short‐term memory (LSTM) networks for modeling temporal dynamics. Our framework effectively learns both the structural evolution and dynamic node interactions in scale‐free networks, allowing accurate prediction of hub emergence. The proposed model is trained on synthetic and real‐world datasets, demonstrating superior predictive accuracy compared to traditional methods. Our findings provide valuable insights into the mechanisms governing hub formation and offer a robust framework for forecasting influential nodes in evolving networks.