First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track

TL;DR

This paper describes the winning solution for the KDD Cup 2021 graph prediction challenge, utilizing Graphormer and ExpC models with ensemble techniques to achieve top performance.

Contribution

The paper introduces a novel ensemble approach combining Graphormer and ExpC models trained on large-scale graph data for competitive graph property prediction.

Findings

Achieved 0.1200 MAE on test set

First place in KDD Cup 2021 graph prediction track

Effective use of ensemble models for large-scale graph tasks

Abstract

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.