# Ensemble Learning for Graph Neural Networks

arXiv: https://arxiv.org/abs/2310.14166 · HTML: https://mlsanity.com/paper/2310.14166/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 22 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [Pairwise Learning for Neural Link Prediction](https://mlsanity.com/paper/2112.02936/) (2021)
- [Adaptive Graph Diffusion Networks](https://mlsanity.com/paper/2012.15024/) (2020)
- [GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction](https://mlsanity.com/paper/2210.01301/) (2022)
- [Path-aware Siamese Graph Neural Network for Link Prediction](https://mlsanity.com/paper/2208.05781/) (2022)

## Built on by (most cited first, 2 of 2)

- [Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?](https://mlsanity.com/paper/2605.22593/) (2026)
- [Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations](https://mlsanity.com/paper/2603.06153/) (2026)

## Abstract (arXiv)

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.