mlsanityData to 7 Oct 2026

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

arXiv 2106.08279 ↗First posted 15 Jun 2021cs.LGcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

10 of 15 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  5. 2017Deep Setscited by 1,394
  6. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  7. 2020Principal Neighbourhood Aggregation for Graph Netscited by 477
  8. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  9. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
  10. 2020Breaking the Expressive Bottlenecks of Graph Neural Networkscited by 6

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Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 2; 2024: 3.

10 papers.