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

06/15/2021
by   Chengxuan Ying, et al.
7

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/12/2021

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...
06/26/2018

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...
10/25/2021

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

In this technical report, we present our approaches for the continual ob...
02/22/2023

BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022

In this technical report, we present our solution for the Baidu KDD Cup ...

Code Repositories

Graphormer

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

Please sign up or login with your details

Forgot password? Click here to reset