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

Xueli Wang, Hongsheng Qian and Peyman Arebi

Complexity, 2025, vol. 2025, 1-18

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5778546

DOI: 10.1155/cplx/5778546

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