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This paper proposes an automated hyperparameter optimization framework based on Differential Evolution for Latent Factorization of Tensors (LFT) to improve prediction accuracy on large-scale dynamic weighted directed networks, reducing the need for manual tuning.
This paper proposes a modular temporal enhancement framework for signed graph neural networks that integrates historical context via a Historical Context Integration Module (HCIM) with LSTM and multi-head temporal attention, achieving consistent improvements on real-world temporal signed networks for dynamic link prediction.