Ensemble Learning for Graph Neural Networks
This work addresses performance and robustness issues in GNNs for researchers and practitioners in graph-based machine learning, but it is incremental as it adapts existing ensemble methods to GNNs.
This paper tackled the problem of improving Graph Neural Networks (GNNs) by applying ensemble learning techniques, resulting in enhanced accuracy and robustness for analyzing graph-structured data.
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.