Papers › Ensemble Learning for Graph Neural Networks
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Property Prediction | ogbl-ddi | ELGNN | Ext. data | No | #2 of 31 | Archive leaderboard | report |
| Link Property Prediction | ogbl-ddi | ELGNN | Number of params | 10512391 | #2 of 31 | Archive leaderboard | report |
| Link Property Prediction | ogbl-ddi | ELGNN | Test Hits@20 | 0.9777 ± 0.0037 | #2 of 31 | Archive leaderboard | report |
| Link Property Prediction | ogbl-ddi | ELGNN | Validation Hits@20 | 0.8965 ± 0.0021 | #2 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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