Graph Neural Networks (GNNs) have shown success in various fields for
learning from graph-structured data. This paper investigates the application of
ensemble learning techniques to improve the performance and robustness of Graph
Neural Networks (GNNs). By training multiple GNN models with diverse
initializations or architectures, we create an ensemble model named ELGNN that
captures various aspects of the data and uses the Tree-Structured Parzen
Estimator algorithm to determine the ensemble weights. Combining the
predictions of these models enhances overall accuracy, reduces bias and
variance, and mitigates the impact of noisy data. Our findings demonstrate the
efficacy of ensemble learning in enhancing GNN capabilities for analyzing
complex graph-structured data. The code is public at
this https URL

本研究探讨了集成学习技术在改善图神经网络（GNNs）性能和鲁棒性方面的应用。我们通过使用多种不同的初始化或结构来训练多个 GNN 模型，创建了一个名为 ELGNN 的集成模型，该模型捕捉了数据的各个方面，并使用树状结构 Parzen 估计算法确定集成权重。结合这些模型的预测结果可以提高整体准确性，减少偏差和方差，并减轻噪声数据的影响。我们的研究结果证明了集成学习在提升 GNN 对分析复杂图结构数据的能力方面的有效性。