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

by   Chengxuan Ying, et al.

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-level track.


page 1

page 2

page 3

page 4


Towards Fine-grained Large Object Segmentation 1st Place Solution to 3D AI Challenge 2020 – Instance Segmentation Track

This technical report introduces our solutions of Team 'FineGrainedSeg' ...

Technical Report of Team GraphMIRAcles in the WikiKG90M-LSC Track of OGB-LSC @ KDD Cup 2021

Link prediction in large-scale knowledge graphs has gained increasing at...

The NIPS'17 Competition: A Multi-View Ensemble Classification Model for Clinically Actionable Genetic Mutations

This paper presents details of our winning solutions to the task IV of N...

Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge

We present our winning solution to the SIIM-ISIC Melanoma Classification...

2nd Place Solution for SODA10M Challenge 2021 – Continual Detection Track

In this technical report, we present our approaches for the continual ob...

Top1 Solution of QQ Browser 2021 Ai Algorithm Competition Track 1 : Multimodal Video Similarity

In this paper, we describe the solution to the QQ Browser 2021 Ai Algori...

Improving random walk rankings with feature selection and imputation

The Science4cast Competition consists of predicting new links in a seman...

Code Repositories


Graphormer is a deep learning package that allows researchers and developers to train custom models for molecule modeling tasks. It aims to accelerate the research and application in AI for molecule science, such as material discovery, drug discovery, etc.

view repo