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Models/RGCN

RGCN

Reported on 12 benchmarks across 1 task · 1 paper · 8 SOTA

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

Graphs12 results

  • Node ClassificationonIMDB (Heterogeneous Node Classification)
    Macro-F1· 2017-03-17
    58.85
    best: 67.53 (RpHGNN)
    SOTA
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonFreebase (Heterogeneous Node Classification)
    Macro-F1· 2017-03-17
    46.78
    best: 54.02 (RpHGNN)
    SOTA
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonDBLP (Heterogeneous Node Classification)
    Macro-F1· 2017-03-17
    91.52
    best: 95.23 (RpHGNN)
    SOTA
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonDBLP (Heterogeneous Node Classification)
    Micro-F1· 2017-03-17
    92.07
    best: 95.55 (RpHGNN)
    SOTA
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonOAG-Venue
    MRR· 2017-03-17
    31.51
    best: 35.46 (RpHGNN)
    SOTA
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonOAG-Venue
    NDCG· 2017-03-17
    48.93
    best: 53.31 (RpHGNN)
    SOTA
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonOAG-L1-Field
    MRR· 2017-03-17
    84.92
    best: 86.79 (RpHGNN)
    SOTA
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonOAG-L1-Field
    NDCG· 2017-03-17
    85.91
    best: 87.8 (RpHGNN)
    SOTA
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonIMDB (Heterogeneous Node Classification)
    Micro-F1· 2017-03-17
    62.05
    best: 69.77 (RpHGNN)
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonFreebase (Heterogeneous Node Classification)
    Micro-F1· 2017-03-17
    58.33
    best: 66.83 (SlotGAT)
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonACM (Heterogeneous Node Classification)
    Macro-F1· 2017-03-17
    91.55
    best: 94.09 (RpHGNN)
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103
  • Node ClassificationonACM (Heterogeneous Node Classification)
    Micro-F1· 2017-03-17
    91.41
    best: 94.06 (SlotGAT)
    Modeling Relational Data with Graph Convolutional NetworksarXiv:1703.06103