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아카이브 평가 64건 · 마지막 적재/변경

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델원본 지표·값논문구현
GLEM+EnGCN
Test Accuracy
0.9014 ± 0.0012
Validation Accuracy
0.9370 ± 0.0004
Number of params
139633805
Ext. data
Yes
Learning on Large-scale Text-attributed Graphs via Variational Inference
2022-10-26
코드 1 · 코드 2
EnGCN
Test Accuracy
0.8798 ± 0.0004
Validation Accuracy
0.9241 ± 0.0003
Number of params
653918
Ext. data
No
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
2022-10-14
코드 1 · 코드 2
GLEM+GIANT+SAGN+SCR
Test Accuracy
0.8737 ± 0.0006
Validation Accuracy
0.9400 ± 0.0003
Number of params
139792525
Ext. data
Yes
Learning on Large-scale Text-attributed Graphs via Variational Inference
2022-10-26
코드 1 · 코드 2
LD+GIANT+SAGN+SCR
Test Accuracy
0.8718 ± 0.0004
Validation Accuracy
0.9399 ± 0.0002
Number of params
110636896
Ext. data
Yes
Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias
2023-09-26
코드 1
**GraDBERT+GIANT & SAGN+SLE+CnS **
Test Accuracy
0.8692 ± 0.0007
Validation Accuracy
0.9371 ± 0.0003
Number of params
1154654
Ext. data
Yes
연결 없음
논문 날짜 미기재
연결 없음
GIANT-XRT+R-SAGN+SCR+C&S
Test Accuracy
0.8684 ± 0.0005
Validation Accuracy
0.9365 ± 0.0003
Number of params
1154142
Ext. data
Yes
연결 없음
논문 날짜 미기재
연결 없음
GIANT-XRT+SAGN+SCR+C&S
Test Accuracy
0.8680 ± 0.0007
Validation Accuracy
0.9357 ± 0.0004
Number of params
1154654
Ext. data
Yes
SCR: Training Graph Neural Networks with Consistency Regularization
2021-12-08
코드 1 · 코드 2 · 코드 3 · 코드 4
GIANT-XRT+SAGN+MCR+C&S
Test Accuracy
0.8673 ± 0.0008
Validation Accuracy
0.9387 ± 0.0002
Number of params
1154654
Ext. data
Yes
SCR: Training Graph Neural Networks with Consistency Regularization
2021-12-08
코드 1 · 코드 2 · 코드 3 · 코드 4
GIANT-XRT+SAGN+SCR
Test Accuracy
0.8667 ± 0.0009
Validation Accuracy
0.9364 ± 0.0005
Number of params
1154654
Ext. data
Yes
SCR: Training Graph Neural Networks with Consistency Regularization
2021-12-08
코드 1 · 코드 2 · 코드 3 · 코드 4
GIANT-XRT+SAGN+MCR
Test Accuracy
0.8651 ± 0.0009
Validation Accuracy
0.9389 ± 0.0002
Number of params
1154654
Ext. data
Yes
SCR: Training Graph Neural Networks with Consistency Regularization
2021-12-08
코드 1 · 코드 2 · 코드 3 · 코드 4
LD+GAMLP
Test Accuracy
0.8645 ± 0.0012
Validation Accuracy
0.9415 ± 0.0003
Number of params
144331677
Ext. data
Yes
Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias
2023-09-26
코드 1
GIANT-XRT+SAGN+SLE+C&S (use raw text)
Test Accuracy
0.8643 ± 0.0020
Validation Accuracy
0.9352 ± 0.0005
Number of params
1548382
Ext. data
Yes
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
2021-10-29
코드 1 · 코드 2 · 코드 3 · 코드 4
GIANT-XRT+SAGN+SLE (use raw text)
Test Accuracy
0.8622 ± 0.0022
Validation Accuracy
0.9363 ± 0.0005
Number of params
1548382
Ext. data
Yes
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
2021-10-29
코드 1 · 코드 2 · 코드 3 · 코드 4
GIANT-XRT+GAMLP+MCR
Test Accuracy
0.8591 ± 0.0008
Validation Accuracy
0.9402 ± 0.0004
Number of params
2144151
Ext. data
Yes
SCR: Training Graph Neural Networks with Consistency Regularization
2021-12-08
코드 1 · 코드 2 · 코드 3 · 코드 4
GAMLP+RLU+SCR+C&S
Test Accuracy
0.8520 ± 0.0008
Validation Accuracy
0.9304 ± 0.0005
Number of params
3335831
Ext. data
No
SCR: Training Graph Neural Networks with Consistency Regularization
2021-12-08
코드 1 · 코드 2 · 코드 3 · 코드 4
GAMLP+RLU+SCR
Test Accuracy
0.8505 ± 0.0009
Validation Accuracy
0.9292 ± 0.0005
Number of params
3335831
Ext. data
No
SCR: Training Graph Neural Networks with Consistency Regularization
2021-12-08
코드 1 · 코드 2 · 코드 3 · 코드 4
SAGN+SLE (4 stages)+C&S
Test Accuracy
0.8485 ± 0.0010
Validation Accuracy
0.9302 ± 0.0003
Number of params
2179678
Ext. data
No
Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training
2021-04-19
코드 1
SAGN+SLE (4 stages)
Test Accuracy
0.8468 ± 0.0012
Validation Accuracy
0.9309 ± 0.0007
Number of params
2179678
Ext. data
No
Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training
2021-04-19
코드 1
GAMLP+MCR
Test Accuracy
0.8462 ± 0.0003
Validation Accuracy
0.9319 ± 0.0003
Number of params
3335831
Ext. data
No
SCR: Training Graph Neural Networks with Consistency Regularization
2021-12-08
코드 1 · 코드 2 · 코드 3 · 코드 4
GAMLP+RLU
Test Accuracy
0.8459 ± 0.0010
Validation Accuracy
0.9324 ± 0.0005
Number of params
3335831
Ext. data
No
연결 없음
논문 날짜 미기재
연결 없음
Spec-MLP-Wide + C&S
Test Accuracy
0.8451 ± 0.0006
Validation Accuracy
0.9132 ± 0.0010
Number of params
406063
Ext. data
No
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
2020-10-27
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7
SAGN+MCR
Test Accuracy
0.8441 ± 0.0005
Validation Accuracy
0.9325 ± 0.0004
Number of params
2179678
Ext. data
No
SCR: Training Graph Neural Networks with Consistency Regularization
2021-12-08
코드 1 · 코드 2 · 코드 3 · 코드 4
SAGN+SLE
Test Accuracy
0.8428 ± 0.0014
Validation Accuracy
0.9287 ± 0.0003
Number of params
2179678
Ext. data
No
Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training
2021-04-19
코드 1
MLP + C&S
Test Accuracy
0.8418 ± 0.0007
Validation Accuracy
0.9147 ± 0.0009
Number of params
96247
Ext. data
No
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
2020-10-27
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7
GIANT-XRT+GraphSAINT(use raw text)
Test Accuracy
0.8415 ± 0.0022
Validation Accuracy
0.9318 ± 0.0004
Number of params
417583
Ext. data
Yes
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
2021-10-29
코드 1 · 코드 2 · 코드 3 · 코드 4
GraphSAGE
Test Accuracy
0.8389 ± 0.0036
Validation Accuracy
0.9242 ± 0.0029
Number of params
433047
Ext. data
No
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
2024-06-13
코드 1
Polynormer
Test Accuracy
0.8382 ± 0.0011
Validation Accuracy
0.9239 ± 0.0005
Number of params
2383654
Ext. data
No
Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
2024-03-02
코드 1 · 코드 2
GAMLP
Test Accuracy
0.8354 ± 0.0009
Validation Accuracy
0.9312 ± 0.0003
Number of params
3335831
Ext. data
No
연결 없음
논문 날짜 미기재
연결 없음
AGDN
Test Accuracy
0.8334 ± 0.0027
Validation Accuracy
0.9229 ± 0.0010
Number of params
1544047
Ext. data
No
Adaptive Graph Diffusion Networks
2020-12-30
코드 1
RevGNN-112
Test Accuracy
0.8307 ± 0.0030
Validation Accuracy
0.9290 ± 0.0007
Number of params
2945007
Ext. data
No
Training Graph Neural Networks with 1000 Layers
2021-06-14
코드 1 · 코드 2 · 코드 3 · 코드 4