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

Edward Elson Kosasih; Joaquin Cabezas; Xavier Sumba; Piotr Bielak; Kamil Tagowski; Kelvin Idanwekhai; Benedict Aaron Tjandra; Arian Rokkum Jamasb

arXiv · 2106.15529

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

Edward Elson Kosasih·Joaquin Cabezas·Xavier Sumba·Piotr Bielak·Kamil Tagowski·Kelvin Idanwekhai·Benedict Aaron Tjandra·Arian Rokkum Jamasb

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Abstract

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.

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Edward Elson Kosasih, Joaquin Cabezas, Xavier Sumba, Piotr Bielak, Kamil Tagowski, Kelvin Idanwekhai, Benedict Aaron Tjandra, Arian Rokkum Jamasb. 2021-06-29. On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction. https://arxiv.org/abs/2106.15529

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