First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track
This is an incremental improvement for graph prediction competitions, specifically in the PCQM4M-LSC track.
The team tackled the KDD Cup 2021 graph prediction track by using Graphormer and ExpC models with ensemble methods, achieving a mean absolute error of 0.1200 on the test set and winning first place.
In this technical report, we present our solution of KDD Cup 2021 OGB Large-Scale Challenge - PCQM4M-LSC Track. We adopt Graphormer and ExpC as our basic models. We train each model by 8-fold cross-validation, and additionally train two Graphormer models on the union of training and validation sets with different random seeds. For final submission, we use a naive ensemble for these 18 models by taking average of their outputs. Using our method, our team MachineLearning achieved 0.1200 MAE on test set, which won the first place in KDD Cup graph prediction track.