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Models/DGN

DGN

Reported on 6 benchmarks across 5 tasks · 2 papers · 4 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Graphs4 results

  • Graph ClassificationonCIFAR10 100k
    Accuracy (%)· 2020-10-06
    72.84
    best: 76.468 (GRIT)
    SOTA
    Directional Graph NetworksarXiv:2010.02863
  • Node ClassificationonPATTERN 100k
    Accuracy (%)· 2020-10-06
    86.68
    best: 86.816 (EGT)
    SOTA
    Directional Graph NetworksarXiv:2010.02863
  • Graph Property Predictiononogbg-molpcba
    Number of params· 2020-10-06
    6732696
    best: 119529665 (HIG(pre-trained on PCQM4M))
    SOTA
    Directional Graph NetworksarXiv:2010.02863
  • Graph Property Predictiononogbg-molhiv
    Number of params· 2020-10-06
    114065
    best: 47183040 (Graphormer)
    Directional Graph NetworksarXiv:2010.02863

Methodology1 result

  • ClassificationonCIFAR10 100k
    Accuracy (%)· 2020-10-06
    72.84
    best: 76.468 (GRIT)
    SOTA
    Directional Graph NetworksarXiv:2010.02863

Computer Vision1 result

  • Image ClassificationonSVHN
    Percentage error· 2014-06-20
    36.02
    best: 1 (E2E-M3)
    Semi-Supervised Learning with Deep Generative ModelsarXiv:1406.5298