mlsanityData to 7 Oct 2026

On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction

arXiv 2106.15529 ↗First posted 29 Jun 2021cs.LGcited by 5

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Abstract (arXiv)

In order to advance large-scale graph machine learning, the Open Graph Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021. The PCQM4M-LSC dataset defines a molecular HOMO-LUMO property prediction task on about 3.8M graphs. In this short paper, we show our current work-in-progress solution which builds an ensemble of three graph neural networks models based on GIN, Bayesian Neural Networks and DiffPool. Our approach outperforms the provided baseline by 7.6%. Moreover, using uncertainty in our ensemble's prediction, we can identify molecules whose HOMO-LUMO gaps are harder to predict (with Pearson's correlation of 0.5181). We anticipate that this will facilitate active learning.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 29 Jun 2021.

Builds on

9 of 15 references.

References · 9

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2018How Powerful are Graph Neural Networks?cited by 4,355
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  5. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  6. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  7. 2015Weight Uncertainty in Neural Networkscited by 554
  8. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  9. 2020Bayesian Graph Neural Networks for Molecular Property Predictioncited by 8

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 1.

5 papers.

  1. 2023GNN-Ensemble: Towards Random Decision Graph Neural Networkscited by 4
  2. 2024Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approachescited by 2
  3. 2023Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Imagescited by 0
  4. 2024E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classificationcited by 0
  5. 2025Enhanced Soups for Graph Neural Networkscited by 0