Ensemble Learning for Graph Neural Networks
Zhen Hao Wong Ling Yue Quanming Yao
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
https://github.com/wongzhenhao/ELGNN.