Papers with Code · CC BY-SA 4.0

Coauthor CS

아카이브 평가 24건 · 마지막 적재/변경 2026-10-30

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
모델	원본 지표·값	논문	구현
NCGCN	Accuracy 96.64 ± 0.29	Clarify Confused Nodes via Separated Learning 2023-06-04	코드 1
NCSAGE	Accuracy 96.48 ± 0.25	Clarify Confused Nodes via Separated Learning 2023-06-04	코드 1
GraphSAGE	Accuracy 96.38±0.11	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification 2024-06-13	코드 1
3ference	Accuracy 95.99%	Inferring from References with Differences for Semi-Supervised Node Classification on Graphs 2022-04-11	코드 1
GNNMoE(GCN-like P)	Accuracy 95.81±0.26	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification 2024-12-11	코드 1
CoLinkDist	Accuracy 95.80%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages 2021-06-16	코드 1 · 코드 2
CoLinkDistMLP	Accuracy 95.74%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages 2021-06-16	코드 1 · 코드 2
GNNMoE(GAT-like P)	Accuracy 95.72±0.23	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification 2024-12-11	코드 1
LinkDistMLP	Accuracy 95.68%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages 2021-06-16	코드 1 · 코드 2
GNNMoE(SAGE-like P)	Accuracy 95.68±0.24	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification 2024-12-11	코드 1
LinkDist	Accuracy 95.66%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages 2021-06-16	코드 1 · 코드 2
HH-GraphSAGE	Accuracy 95.13%	Half-Hop: A graph upsampling approach for slowing down message passing 2023-08-17	코드 1
GraphSAGE	Accuracy 95.11%	Half-Hop: A graph upsampling approach for slowing down message passing 2023-08-17	코드 1
Exphormer	Accuracy 94.93±0.46%	Exphormer: Sparse Transformers for Graphs 2023-03-10	코드 1
GCN-LPA	Accuracy 94.8 ± 0.4	Unifying Graph Convolutional Neural Networks and Label Propagation 2020-02-17	코드 1 · 코드 2
HH-GCN	Accuracy 94.71%	Half-Hop: A graph upsampling approach for slowing down message passing 2023-08-17	코드 1
GCN	Accuracy 94.06%	Half-Hop: A graph upsampling approach for slowing down message passing 2023-08-17	코드 1
GCN (PPR Diffusion)	Accuracy 93.01%	Diffusion Improves Graph Learning 2019-10-28	코드 1 · 코드 2 · 코드 3
DAGNN (Ours)	Accuracy 92.8%	Towards Deeper Graph Neural Networks 2020-07-18	코드 1 · 코드 2 · 코드 3
SIGN	Accuracy 91.98 ± 0.50	SIGN: Scalable Inception Graph Neural Networks 2020-04-23	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
GraphMix (GCN)	Accuracy 91.83 ± 0.51	GraphMix: Improved Training of GNNs for Semi-Supervised Learning 2019-09-25	코드 1
Graph InfoClust (GIC)	Accuracy 89.4 ± 0.4	Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning 2020-09-15	코드 1 · 코드 2
SNoRe	Accuracy 88.7%	SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations 2020-09-08	코드 1
FIT-GNN	Inference Time (ms) 0.0017	FIT-GNN: Faster Inference Time for GNNs Using Coarsening 2024-10-19	코드 1