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Models/GCN

GCN

Reported on 100 benchmarks across 19 tasks · 11 papers · 23 SOTA

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

Medical44 results

Drug Discoveryontdcommons
TDC.Caco2_Wang· 2021-02-18
0.599
SOTA
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.Half_Life_Obach· 2021-02-18
0.239
best: 0.396 (XGBoost)
SOTA
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.PPBR_AZ· 2021-02-18
10.194
SOTA
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.Caco2_Wang· 2021-02-18
0.599
SOTA
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.Half_Life_Obach· 2021-02-18
0.239
best: 0.396 (XGBoost)
SOTA
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.PPBR_AZ· 2021-02-18
10.194
SOTA
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.AMES· 2021-02-18
0.818
best: 0.859 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.BBB_Martins· 2021-02-18
0.842
best: 0.905 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.Bioavailability_Ma· 2021-02-18
0.566
best: 0.7 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.CYP2C9_Inhibition_Veith· 2021-02-18
0.735
best: 0.877 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.CYP2C9_Substrate_CarbonMangels· 2021-02-18
0.344
best: 0.68 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.CYP2D6_Inhibition_Veith· 2021-02-18
0.616
best: 0.794 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.CYP2D6_Substrate_CarbonMangels· 2021-02-18
0.617
best: 0.704 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.CYP3A4_Inhibition_Veith· 2021-02-18
0.84
best: 0.902 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.CYP3A4_Substrate_CarbonMangels· 2021-02-18
0.59
best: 0.648 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.Clearance_Hepatocyte_AZ· 2021-02-18
0.366
best: 0.587 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.Clearance_Microsome_AZ· 2021-02-18
0.532
best: 0.586 (MLP-RDKit2D)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.DILI· 2021-02-18
0.859
best: 0.933 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.HIA_Hou· 2021-02-18
0.936
best: 0.987 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.LD50_Zhu· 2021-02-18
0.649
best: 0.685 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.Lipophilicity_AstraZeneca· 2021-02-18
0.541
best: 0.574 (MLP-RDKit2D)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.Pgp_Broccatelli· 2021-02-18
0.895
best: 0.929 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.Solubility_AqSolDB· 2021-02-18
0.907
best: 1.026 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.VDss_Lombardo· 2021-02-18
0.457
best: 0.612 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Drug Discoveryontdcommons
TDC.hERG· 2021-02-18
0.738
best: 0.841 (MLP-RDKit2D)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.AMES· 2021-02-18
0.818
best: 0.859 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.BBB_Martins· 2021-02-18
0.842
best: 0.905 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.Bioavailability_Ma· 2021-02-18
0.566
best: 0.7 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.CYP2C9_Inhibition_Veith· 2021-02-18
0.735
best: 0.877 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.CYP2C9_Substrate_CarbonMangels· 2021-02-18
0.344
best: 0.68 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.CYP2D6_Inhibition_Veith· 2021-02-18
0.616
best: 0.794 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.CYP2D6_Substrate_CarbonMangels· 2021-02-18
0.617
best: 0.704 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.CYP3A4_Inhibition_Veith· 2021-02-18
0.84
best: 0.902 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.CYP3A4_Substrate_CarbonMangels· 2021-02-18
0.59
best: 0.648 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.Clearance_Hepatocyte_AZ· 2021-02-18
0.366
best: 0.587 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.Clearance_Microsome_AZ· 2021-02-18
0.532
best: 0.586 (MLP-RDKit2D)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.DILI· 2021-02-18
0.859
best: 0.933 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.HIA_Hou· 2021-02-18
0.936
best: 0.987 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.LD50_Zhu· 2021-02-18
0.649
best: 0.685 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.Lipophilicity_AstraZeneca· 2021-02-18
0.541
best: 0.574 (MLP-RDKit2D)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.Pgp_Broccatelli· 2021-02-18
0.895
best: 0.929 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.Solubility_AqSolDB· 2021-02-18
0.907
best: 1.026 (AttrMasking)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.VDss_Lombardo· 2021-02-18
0.457
best: 0.612 (XGBoost)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548
Therapeutics Data Commonsontdcommons
TDC.hERG· 2021-02-18
0.738
best: 0.841 (MLP-RDKit2D)
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development arXiv:2102.09548

Graphs28 results

Graph RegressiononPCQM4M-LSC
Test MAE· 2021-03-17
18.38
best: 13.28 (Graphormer )
SOTA
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs arXiv:2103.09430
Graph RegressiononPCQM4M-LSC
Validation MAE· 2021-03-17
0.1684
best: 0.1148 (O-GNN)
SOTA
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs arXiv:2103.09430
Graph RegressiononPCQM4Mv2-LSC
Test MAE· 2016-09-09
0.1398
best: 0.0683 (EGT + Triangular Attention)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Graph RegressiononPCQM4Mv2-LSC
Validation MAE· 2016-09-09
0.1379
best: 0.0235 (ESA (Edge set attention, no positional encodings))
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonCiteseer
Accuracy· 2016-09-09
70.3
best: 76.33 (3ference)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonNELL
Accuracy· 2016-09-09
66
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonPubmed
Accuracy· 2016-09-09
79
best: 88.9 (3ference)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonIMDB (Heterogeneous Node Classification)
Macro-F1· 2016-09-09
57.88
best: 67.53 (RpHGNN)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonIMDB (Heterogeneous Node Classification)
Micro-F1· 2016-09-09
64.82
best: 69.77 (RpHGNN)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonFreebase (Heterogeneous Node Classification)
Macro-F1· 2016-09-09
27.84
best: 54.02 (RpHGNN)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonFreebase (Heterogeneous Node Classification)
Micro-F1· 2016-09-09
60.23
best: 66.83 (SlotGAT)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonDBLP (Heterogeneous Node Classification)
Macro-F1· 2016-09-09
90.84
best: 95.23 (RpHGNN)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonDBLP (Heterogeneous Node Classification)
Micro-F1· 2016-09-09
91.47
best: 95.55 (RpHGNN)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonACM (Heterogeneous Node Classification)
Macro-F1· 2016-09-09
92.17
best: 94.09 (RpHGNN)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node ClassificationonACM (Heterogeneous Node Classification)
Micro-F1· 2016-09-09
92.12
best: 94.06 (SlotGAT)
SOTA
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node Property Predictiononogbn-arxiv
Number of params· 2024-06-13
1463336
best: 1386219488 (SimTeG+TAPE+RevGAT)
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification arXiv:2406.08993
Node Property Predictiononogbn-products
Number of params· 2024-06-13
233047
best: 313612207 (Node2vec)
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification arXiv:2406.08993
Node ClassificationonWiki-CS
Accuracy· 2023-08-17
81.93
best: 83.67 (GraphSAGE)
Half-Hop: A graph upsampling approach for slowing down message passing arXiv:2308.09198
Link Property Predictiononogbl-ddi
Number of params· 2016-09-09
1289985
best: 976022023 (HyperFusion)
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Link Property Predictiononogbl-collab
Number of params· 2016-09-09
296449
best: 1064446212 (HyperFusion)
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Link Property Predictiononogbl-ppa
Number of params· 2016-09-09
278529
best: 295848449 (Refined-GAE)
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Graph Property Predictiononogbg-molhiv
Number of params· 2016-09-09
527701
best: 47183040 (Graphormer)
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Graph Property Predictiononogbg-code2
Number of params· 2016-09-09
11033210
best: 63684290 (GMAN+bag of tricks)
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Graph Property Predictiononogbg-ppa
Number of params· 2016-09-09
479437
best: 16346166 (PAS+F2GNN)
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Graph Property Predictiononogbg-molpcba
Number of params· 2016-09-09
565928
best: 119529665 (HIG(pre-trained on PCQM4M))
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node Property Predictiononogbn-arxiv
Number of params· 2016-09-09
110120
best: 1386219488 (SimTeG+TAPE+RevGAT)
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Node Property Predictiononogbn-proteins
Number of params· 2016-09-09
96880
best: 664233700 (LD+GAT)
Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907
Graph Question AnsweringonGQA
Accuracy
85.7
best: 96.3 (GraphVQA)

Natural Language Processing12 results

Stance DetectiononMGTAB
Acc· 2023-01-03
82.4
best: 87.8 (RGT)
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark arXiv:2301.01123
Stance DetectiononMGTAB
F1· 2023-01-03
81.5
best: 86.9 (RGT)
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark arXiv:2301.01123
Data-to-Text GenerationonWebNLG 2.0 (Unconstrained)
BLEU· 2022-04-13
60.8
best: 66.2 (GAP - Me,r+γ)
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation arXiv:2204.06674
Data-to-Text GenerationonWebNLG 2.0 (Unconstrained)
METEOR· 2022-04-13
42.76
best: 47.25 (JointGT (T5))
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation arXiv:2204.06674
Data-to-Text GenerationonWebNLG 2.0 (Unconstrained)
ROUGE· 2022-04-13
71.13
best: 76.36 (GAP - Me,r+γ)
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation arXiv:2204.06674
KG-to-Text GenerationonWebNLG 2.0 (Unconstrained)
BLEU· 2022-04-13
60.8
best: 66.2 (GAP - Me,r+γ)
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation arXiv:2204.06674
KG-to-Text GenerationonWebNLG 2.0 (Unconstrained)
METEOR· 2022-04-13
42.76
best: 47.25 (JointGT (T5))
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation arXiv:2204.06674
KG-to-Text GenerationonWebNLG 2.0 (Unconstrained)
ROUGE· 2022-04-13
71.13
best: 76.36 (GAP - Me,r+γ)
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation arXiv:2204.06674
Named Entity Recognition (NER)onCoNLL 2003 (English)
F1· 2021-12-15
88.63
best: 94.6 (ACE + document-context)
Named entity recognition architecture combining contextual and global features arXiv:2112.08033
Relation ExtractiononTACRED
F1· 2018-09-26
64
best: 86.6 (RAG4RE)
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction arXiv:1809.10185
Relation ExtractiononACE 2005
NER Micro F1
84.2
best: 91.3 (ASP+T5-3B)
Relation ExtractiononACE 2005
RE+ Micro F1
59.1
best: 71.1 (PL-Marker)

Methodology6 results

Explainable artificial intelligenceonMUTAG
fidelity
0.702
Explainable artificial intelligenceonSST2
fidelity
0.373
Explainable artificial intelligenceonBA-2motifs
fidelity
0.549
Explainable artificial intelligenceonBBBP
fidelity
0.489
Explainable artificial intelligenceonSST-5
fidelity
0.393
Explainable artificial intelligenceonBA-Shapes
fidelity
0.214

Miscellaneous4 results

Fraud DetectiononElliptic Dataset
AUC· 2024-05-29
0.8329
SOTA
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation arXiv:2405.19383
Fraud DetectiononElliptic Dataset
AUPRC· 2024-05-29
0.5946
best: 0.6312 (GraphSAGE)
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation arXiv:2405.19383
Twitter Bot DetectiononMGTAB
Acc· 2023-01-03
85.8
best: 92.1 (RGT)
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark arXiv:2301.01123
Twitter Bot DetectiononMGTAB
F1· 2023-01-03
78.3
best: 90.4 (RGT)
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark arXiv:2301.01123

Adversarial3 results

Text GenerationonWebNLG 2.0 (Unconstrained)
BLEU· 2022-04-13
60.8
best: 66.2 (GAP - Me,r+γ)
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation arXiv:2204.06674
Text GenerationonWebNLG 2.0 (Unconstrained)
METEOR· 2022-04-13
42.76
best: 47.25 (JointGT (T5))
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation arXiv:2204.06674
Text GenerationonWebNLG 2.0 (Unconstrained)
ROUGE· 2022-04-13
71.13
best: 76.36 (GAP - Me,r+γ)
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation arXiv:2204.06674

Robots2 results

Active Speaker DetectiononElliptic Dataset
AUC· 2024-05-29
0.8329
SOTA
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation arXiv:2405.19383
Active Speaker DetectiononElliptic Dataset
AUPRC· 2024-05-29
0.5946
best: 0.6312 (GraphSAGE)
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation arXiv:2405.19383

Computer Vision2 results

Zero-Shot Action RecognitiononKinetics
Top-1 Accuracy· 2020-08-28
22.3
best: 78.1 (TC-CLIP)
All About Knowledge Graphs for Actions arXiv:2008.12432
Zero-Shot Action RecognitiononKinetics
Top-5 Accuracy· 2020-08-28
49.7
best: 95.7 (TC-CLIP)
All About Knowledge Graphs for Actions arXiv:2008.12432