自动优化张量因子分解的超参数，提升大规模动态网络建模精度。
Hyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network
- 用差分进化算法自动学习张量分解的正则化参数
- 在4个真实数据集上实现更低的MAE和RMSE
- 适合需要减少人工调参的动态网络分析场景
大规模动态加权有向网络（DWDNs）广泛用于建模节点间随时间变化的交互。张量因子分解（LFT）通过低秩嵌入从DWDNs中提取目标知识。然而，与许多机器学习模型类似，LFT的性能高度依赖于超参数选择。实践中这些参数常通过手动或网格搜索调整，耗时耗力且需大量计算资源。为此，本文提出基于差分进化（DE）的自动化超参数优化框架DE-LFT，将DE融入LFT训练过程，自动学习最优正则化参数λ₁、λ₂和λ₃。该方法可自适应搜索超参数空间，提升预测精度。在四个真实数据集上的实验表明，所提方法相比手动调参基线，取得了更低的MAE和RMSE，同时大幅减少对繁琐调参的需求。
原文摘要 · Abstract (English)
Large-scale dynamic weighted directed networks (DWDNs) are widely used to model time-varying interactions among nodes. Latent factorization of tensors (LFT) extracts target knowledge from DWDNs via low-rank embedding. However, similar to many machine learning models, the performance of LFT heavily depends on the selection of hyperparameters. In practice, these parameters are often tuned manually or through grid search, which requires significant computational resources and human effort. Motivated by this challenge, this paper proposes an automated hyperparameter optimization framework based on Differential Evolution (DE) for LFT (DE-LFT). The proposed method integrates DE into the training process of the LFT model to automatically learn optimal regularization parameters $λ_1$, $λ_2$ and $λ_3$. As a result, the model can adaptively search the hyperparameter space and improve prediction accuracy. Experimental results on four real-world datasets demonstrate that the proposed approach achieves lower MAE and RMSE compared with manually tuned baselines while reducing the need for extensive parameter tuning.
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