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Models/GraphSAGE

GraphSAGE

Reported on 21 benchmarks across 7 tasks · 5 papers · 7 SOTA

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

Graphs13 results

  • Node ClassificationonWiki-CS
    Accuracy· 2023-08-17
    83.67
    SOTA
    Half-Hop: A graph upsampling approach for slowing down message passingarXiv:2308.09198
  • Graph ClassificationonREDDIT-MULTI-5k
    Accuracy· 2019-12-20
    50
    SOTA
    A Fair Comparison of Graph Neural Networks for Graph ClassificationarXiv:1912.09893
  • Node ClassificationonPPI
    F1· 2017-06-07
    61.2
    best: 99.71 (g2-MLP)
    SOTA
    Inductive Representation Learning on Large GraphsarXiv:1706.02216
  • Node ClassificationonCiteSeer with Public Split: fixed 20 nodes per class
    Accuracy· 2017-06-07
    67.2
    best: 77.5 (OGC)
    SOTA
    Inductive Representation Learning on Large GraphsarXiv:1706.02216
  • Node Property Predictiononogbn-arxiv
    Number of params· 2024-06-13
    1727272
    best: 1386219488 (SimTeG+TAPE+RevGAT)
    Classic GNNs are Strong Baselines: Reassessing GNNs for Node ClassificationarXiv:2406.08993
  • Node Property Predictiononogbn-products
    Number of params· 2024-06-13
    433047
    best: 313612207 (Node2vec)
    Classic GNNs are Strong Baselines: Reassessing GNNs for Node ClassificationarXiv:2406.08993
  • Node Property Predictiononogbn-proteins
    Number of params· 2024-06-13
    2444896
    best: 664233700 (LD+GAT)
    Classic GNNs are Strong Baselines: Reassessing GNNs for Node ClassificationarXiv:2406.08993
  • Graph ClassificationonREDDIT-B
    Accuracy· 2019-12-20
    84.3
    best: 93.15 (CRaWl)
    A Fair Comparison of Graph Neural Networks for Graph ClassificationarXiv:1912.09893
  • Link Property Predictiononogbl-ddi
    Number of params· 2017-06-07
    1421057
    best: 976022023 (HyperFusion)
    Inductive Representation Learning on Large GraphsarXiv:1706.02216
  • Link Property Predictiononogbl-collab
    Number of params· 2017-06-07
    460289
    best: 1064446212 (HyperFusion)
    Inductive Representation Learning on Large GraphsarXiv:1706.02216
  • Link Property Predictiononogbl-ppa
    Number of params· 2017-06-07
    424449
    best: 295848449 (Refined-GAE)
    Inductive Representation Learning on Large GraphsarXiv:1706.02216
  • Node Property Predictiononogbn-arxiv
    Number of params· 2017-06-07
    218664
    best: 1386219488 (SimTeG+TAPE+RevGAT)
    Inductive Representation Learning on Large GraphsarXiv:1706.02216
  • Node Property Predictiononogbn-proteins
    Number of params· 2017-06-07
    193136
    best: 664233700 (LD+GAT)
    Inductive Representation Learning on Large GraphsarXiv:1706.02216

Miscellaneous4 results

  • Fraud DetectiononElliptic Dataset
    AUPRC· 2024-05-29
    0.6312
    SOTA
    Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental EvaluationarXiv:2405.19383
  • Fraud DetectiononElliptic Dataset
    AUC· 2024-05-29
    0.8279
    best: 0.8329 (GCN)
    Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental EvaluationarXiv:2405.19383
  • Fraud DetectiononHealthcare Provider Fraud Detection Analysis
    AUC
    0.668
    best: 0.786 (BiRank)
  • Fraud DetectiononHealthcare Provider Fraud Detection Analysis
    AUPRC
    0.201

Robots4 results

  • Active Speaker DetectiononElliptic Dataset
    AUPRC· 2024-05-29
    0.6312
    SOTA
    Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental EvaluationarXiv:2405.19383
  • Active Speaker DetectiononElliptic Dataset
    AUC· 2024-05-29
    0.8279
    best: 0.8329 (GCN)
    Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental EvaluationarXiv:2405.19383
  • Active Speaker DetectiononHealthcare Provider Fraud Detection Analysis
    AUC
    0.668
    best: 0.786 (BiRank)
  • Active Speaker DetectiononHealthcare Provider Fraud Detection Analysis
    AUPRC
    0.201

Methodology2 results

  • ClassificationonREDDIT-MULTI-5k
    Accuracy· 2019-12-20
    50
    SOTA
    A Fair Comparison of Graph Neural Networks for Graph ClassificationarXiv:1912.09893
  • ClassificationonREDDIT-B
    Accuracy· 2019-12-20
    84.3
    best: 93.15 (CRaWl)
    A Fair Comparison of Graph Neural Networks for Graph ClassificationarXiv:1912.09893