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Models/GNN

GNN

Reported on 55 benchmarks across 11 tasks · 5 papers · 25 SOTA

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

Computer Vision14 results

Cross-Domain Few-ShotonChestX
5 shot· 2017-11-10
25.27
best: 26.24 (StyleAdv-FT)
SOTA
Few-Shot Learning with Graph Neural Networks arXiv:1711.04043
Cross-Domain Few-ShotonEuroSAT
5 shot· 2017-11-10
83.64
best: 91.64 (StyleAdv-FT)
SOTA
Few-Shot Learning with Graph Neural Networks arXiv:1711.04043
Cross-Domain Few-ShotonISIC2018
5 shot· 2017-11-10
43.94
best: 53.05 (StyleAdv-FT)
SOTA
Few-Shot Learning with Graph Neural Networks arXiv:1711.04043
Visual DialogonVisDial v0.9 val
MRR· 2019-04-11
0.6285
best: 68.92 (9xFGA (VGG))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisDial v0.9 val
Mean Rank· 2019-04-11
4.57
best: 5.84 (HieCoAtt-QI)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisDial v0.9 val
R@1· 2019-04-11
48.95
best: 55.16 (9xFGA (VGG))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisDial v0.9 val
R@10· 2019-04-11
88.36
best: 92.95 (9xFGA (VGG))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisDial v0.9 val
R@5· 2019-04-11
79.65
best: 86.26 (9xFGA (VGG))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisual Dialog v1.0 test-std
MRR (x 100)· 2019-04-11
61.37
best: 71.24 (MRR ensemble (Naive))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisual Dialog v1.0 test-std
Mean· 2019-04-11
4.57
best: 49.61 (qqhe)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisual Dialog v1.0 test-std
NDCG (x 100)· 2019-04-11
52.82
best: 78.7 (Single)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisual Dialog v1.0 test-std
R@1· 2019-04-11
47.33
best: 58.3 (2 Step: Factor Graph Attention + VD-Bert)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisual Dialog v1.0 test-std
R@10· 2019-04-11
87.83
best: 95.08 (Ensemble FGA + BERT)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
Visual DialogonVisual Dialog v1.0 test-std
R@5· 2019-04-11
77.98
best: 88.42 (Ensemble FGA + BERT)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548

Speech11 results

DialogueonVisDial v0.9 val
MRR· 2019-04-11
0.6285
best: 68.92 (9xFGA (VGG))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisDial v0.9 val
Mean Rank· 2019-04-11
4.57
best: 5.84 (HieCoAtt-QI)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisDial v0.9 val
R@1· 2019-04-11
48.95
best: 55.16 (9xFGA (VGG))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisDial v0.9 val
R@10· 2019-04-11
88.36
best: 92.95 (9xFGA (VGG))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisDial v0.9 val
R@5· 2019-04-11
79.65
best: 86.26 (9xFGA (VGG))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisual Dialog v1.0 test-std
MRR (x 100)· 2019-04-11
61.37
best: 71.24 (MRR ensemble (Naive))
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisual Dialog v1.0 test-std
Mean· 2019-04-11
4.57
best: 49.61 (qqhe)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisual Dialog v1.0 test-std
NDCG (x 100)· 2019-04-11
52.82
best: 78.7 (Single)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisual Dialog v1.0 test-std
R@1· 2019-04-11
47.33
best: 58.3 (2 Step: Factor Graph Attention + VD-Bert)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisual Dialog v1.0 test-std
R@10· 2019-04-11
87.83
best: 95.08 (Ensemble FGA + BERT)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548
DialogueonVisual Dialog v1.0 test-std
R@5· 2019-04-11
77.98
best: 88.42 (Ensemble FGA + BERT)
Reasoning Visual Dialogs with Structural and Partial Observations arXiv:1904.05548

Natural Language Processing10 results

Data-to-Text GenerationonWikiGraphs
Test perplexity· 2021-07-20
26.93
best: 25.85 (Unconditional)
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
Data-to-Text GenerationonWikiGraphs
rBLEU (Test)· 2021-07-20
26.22
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
Data-to-Text GenerationonWikiGraphs
rBLEU (Valid)· 2021-07-20
31.39
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
Data-to-Text GenerationonWikiGraphs
rBLEU(w/title)(Test)· 2021-07-20
28.35
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
Data-to-Text GenerationonWikiGraphs
rBLEU(w/title)(Valid)· 2021-07-20
32.65
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
KG-to-Text GenerationonWikiGraphs
Test perplexity· 2021-07-20
26.93
best: 25.85 (Unconditional)
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
KG-to-Text GenerationonWikiGraphs
rBLEU (Test)· 2021-07-20
26.22
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
KG-to-Text GenerationonWikiGraphs
rBLEU (Valid)· 2021-07-20
31.39
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
KG-to-Text GenerationonWikiGraphs
rBLEU(w/title)(Test)· 2021-07-20
28.35
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
KG-to-Text GenerationonWikiGraphs
rBLEU(w/title)(Valid)· 2021-07-20
32.65
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556

Miscellaneous8 results

MS/MS spectrum simulationonMassSpecGym
Cosine Similarity· 2024-10-30
0.19
best: 0.52 (FraGNNet)
MassSpecGym: A benchmark for the discovery and identification of molecules arXiv:2410.23326
MS/MS spectrum simulationonMassSpecGym
Hit Rate @ 1· 2024-10-30
3.95
best: 46.64 (FraGNNet)
MassSpecGym: A benchmark for the discovery and identification of molecules arXiv:2410.23326
MS/MS spectrum simulationonMassSpecGym
Hit Rate @ 20· 2024-10-30
26.27
best: 83.58 (FraGNNet)
MassSpecGym: A benchmark for the discovery and identification of molecules arXiv:2410.23326
MS/MS spectrum simulationonMassSpecGym
Hit Rate @ 5· 2024-10-30
11.92
best: 72.56 (FraGNNet)
MassSpecGym: A benchmark for the discovery and identification of molecules arXiv:2410.23326
MS/MS spectrum simulationonMassSpecGym
Jensen-Shannon Similarity· 2024-10-30
0.2
best: 0.47 (FraGNNet)
MassSpecGym: A benchmark for the discovery and identification of molecules arXiv:2410.23326
MS/MS spectrum simulation (bonus chemical formulae)onMassSpecGym
Hit Rate @ 1· 2024-10-30
3.63
best: 31.93 (FraGNNet)
MassSpecGym: A benchmark for the discovery and identification of molecules arXiv:2410.23326
MS/MS spectrum simulation (bonus chemical formulae)onMassSpecGym
Hit Rate @ 20· 2024-10-30
33.77
best: 82.7 (FraGNNet)
MassSpecGym: A benchmark for the discovery and identification of molecules arXiv:2410.23326
MS/MS spectrum simulation (bonus chemical formulae)onMassSpecGym
Hit Rate @ 5· 2024-10-30
13.55
best: 63.2 (FraGNNet)
MassSpecGym: A benchmark for the discovery and identification of molecules arXiv:2410.23326

Methodology6 results

Few-Shot LearningonChestX
5 shot· 2017-11-10
25.27
best: 26.24 (StyleAdv-FT)
SOTA
Few-Shot Learning with Graph Neural Networks arXiv:1711.04043
Few-Shot LearningonEuroSAT
5 shot· 2017-11-10
83.64
best: 91.64 (StyleAdv-FT)
SOTA
Few-Shot Learning with Graph Neural Networks arXiv:1711.04043
Few-Shot LearningonISIC2018
5 shot· 2017-11-10
43.94
best: 53.05 (StyleAdv-FT)
SOTA
Few-Shot Learning with Graph Neural Networks arXiv:1711.04043
Meta-LearningonChestX
5 shot· 2017-11-10
25.27
best: 26.24 (StyleAdv-FT)
SOTA
Few-Shot Learning with Graph Neural Networks arXiv:1711.04043
Meta-LearningonEuroSAT
5 shot· 2017-11-10
83.64
best: 91.64 (StyleAdv-FT)
SOTA
Few-Shot Learning with Graph Neural Networks arXiv:1711.04043
Meta-LearningonISIC2018
5 shot· 2017-11-10
43.94
best: 53.05 (StyleAdv-FT)
SOTA
Few-Shot Learning with Graph Neural Networks arXiv:1711.04043

Adversarial5 results

Text GenerationonWikiGraphs
Test perplexity· 2021-07-20
26.93
best: 25.85 (Unconditional)
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
Text GenerationonWikiGraphs
rBLEU (Test)· 2021-07-20
26.22
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
Text GenerationonWikiGraphs
rBLEU (Valid)· 2021-07-20
31.39
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
Text GenerationonWikiGraphs
rBLEU(w/title)(Test)· 2021-07-20
28.35
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556
Text GenerationonWikiGraphs
rBLEU(w/title)(Valid)· 2021-07-20
32.65
SOTA
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset arXiv:2107.09556

Graphs1 result

Community DetectiononAmazon
Accuracy-NE· uses extra data· 2017-05-23
2
SOTA
Supervised Community Detection with Line Graph Neural Networks arXiv:1705.08415