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Paper · 2106.08279 · 2021

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

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 6 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
dpstart/graphormer_new pwc_unofficial 4 of 5
ytchx1999/Graphormer pwc_unofficial 1 of 1
FunctionStatusWhere it lives
convert_to_single_emb Ran dpstart/graphormer_new/graphormer/wrapper.py
pointer only (licence: MIT) · get_code("29264267bc4ef9cf")
pad_1d_unsqueeze Ran dpstart/graphormer_new/graphormer/collator.py
pointer only (licence: MIT) · get_code("ed9c8a17fdb3d49c")
pad_2d_bool Ran dpstart/graphormer_new/graphormer/collator.py
pointer only (licence: MIT) · get_code("76af4bc12f28e3b2")
pad_2d_unsqueeze Ran dpstart/graphormer_new/graphormer/collator.py
pointer only (licence: MIT) · get_code("28715c03e155eec6")
pad_attn_bias_unsqueeze Ran ytchx1999/Graphormer/graphormer/collator.py
pointer only (licence: MIT) · get_code("b5df9592a75e3bb9")
flag_bounded Not yet run dpstart/graphormer_new/graphormer/utils/flag.py
pointer only (licence: MIT) · get_code("f08fd41391ce55ac")

Repositories linked to this paper

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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.

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