graph learning code for ogb

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Computer Vision YouGraph
Overview

The final code for OGB

Installation Requirements:

  1. ogb=1.3.1
  2. torch=1.7.0
  3. torch-geometric=1.7.0
  4. torch-scatter=2.0.6
  5. torch-sparse=0.6.9

Baseline models

  1. The path of ogbn-ppa dataset should be ogb/ppa/dataset/ogbn_ppa, then you can get the performance reported by running the default code 10 times. In fact, we can get 82 by other parameters, but the model will be too large and training is much more slower. So in this report, we only set the parameters for the performance 81.4.
  2. The path of ogbn-code2 dataset should be ogb/code/dataset/ogbn_code2. Then you can get the performance reported by running the default code 10 times

ogbg-ppa

  1. cd ogb/ppa;
  2. sh run_script.sh 0

ogbg-code2

  1. cd ogb/code;
  2. sh run_script.sh 0

Performance

bot : bag of tricks

ogbg-ppa

methods test accuracy val accuracy Parameters Hardware
ExpC+bot 0.8140±0.0028 0.7811±0.0012 3.7M Tesla V100 32GB

ogbg-code2

methods test accuracy val accuracy Parameters Hardware
GMAN+bot 0.1770±0.0012 0.1631±0.0090 63.7M Tesla V100 32GB

Reference:

  1. https://github.com/qslim/epcb-gnns
  2. https://github.com/devnkong/FLAG
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Comments
  • About OGB submission

    About OGB submission

    Thanks for the submission.

    Your method is called "ExpC + bag of tricks" but the "bag of tricks" is ambiguous. I guess you meant this paper (https://arxiv.org/pdf/2103.13355.pdf). You will need to write a specialized technical report, clearly explaining how you exactly implement your method.

    opened by weihua916 3
Owner
PierreHao
PierreHao
GitHub
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