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Cora

아카이브 평가 73건 · 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
SSP	Accuracy 90.16% ± 0.59%	Optimization of Graph Neural Networks with Natural Gradient Descent 2020-08-21	코드 1
SplineCNN	Accuracy 89.48% ± 0.31%	SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels 2017-11-24	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
ACMII-Snowball-3	Accuracy 89.36% ± 1.26%	Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? 2021-09-12	연결 없음
ACMII-GCN	Accuracy 88.95% ± 1.04%	Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? 2021-09-12	연결 없음
ACM-Snowball-2	Accuracy 88.83% ± 1.49%	Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? 2021-09-12	연결 없음
UGT	Accuracy 88.74±0.6%	Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity 2023-08-18	코드 1 · 코드 2 · 코드 3
GAT + SWA	Accuracy 88.66 ± 1.38%	The Split Matters: Flat Minima Methods for Improving the Performance of GNNs 2023-06-15	코드 1
ACM-GCN	Accuracy 88.62% ± 1.22%	Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? 2021-09-12	연결 없음
GCN-LPA	Accuracy 88.5% ± 1.5%	Unifying Graph Convolutional Neural Networks and Label Propagation 2020-02-17	코드 1 · 코드 2
LinkDist	Accuracy 88.24%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages 2021-06-16	코드 1 · 코드 2
CNMPGNN	Accuracy 88.20±1.22%	CN-Motifs Perceptive Graph Neural Networks 2021-11-15	연결 없음
CoLinkDist	Accuracy 87.89%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages 2021-06-16	코드 1 · 코드 2
3ference	Accuracy 87.78%	Inferring from References with Differences for Semi-Supervised Node Classification on Graphs 2022-04-11	코드 1
LinkDistMLP	Accuracy 87.58%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages 2021-06-16	코드 1 · 코드 2
CoLinkDistMLP	Accuracy 87.54%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages 2021-06-16	코드 1 · 코드 2
AS-GCN	Accuracy 87.44% ± 0.0034%	Adaptive Sampling Towards Fast Graph Representation Learning 2018-09-14	코드 1 · 코드 2
CGT	Accuracy 87.10±1.53	Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures 2023-12-28	코드 1
NodeNet	Accuracy 86.80%	NodeNet: A Graph Regularised Neural Network for Node Classification 2020-06-16	연결 없음
Cleora	Accuracy 86.80%	Cleora: A Simple, Strong and Scalable Graph Embedding Scheme 2021-02-03	코드 1 · 코드 2
DFNet-ATT	Accuracy 86% ± 0.4%	DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters 2019-10-24	코드 1
MMA	Accuracy 85.80%	Multi-Mask Aggregators for Graph Neural Networks 2022-11-24	코드 1
GResNet(GAT)	Accuracy 85.5%	GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation 2019-09-12	코드 1 · 코드 2
AdaGCN	Accuracy 85.46% ± 0.25%	AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models 2019-08-14	코드 1
TDGNN	Accuracy 85.35% ± 0.49%	Tree Decomposed Graph Neural Network 2021-08-25	코드 1
PPNP	Accuracy 85.29% ± 0.25% Validation YES	Predict then Propagate: Graph Neural Networks meet Personalized PageRank 2018-10-14	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
DifNet	Accuracy 85.1%	Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification 2020-01-22	코드 1 · 코드 2
APPNP	Accuracy 85.09% ± 0.25% Validation YES	Predict then Propagate: Graph Neural Networks meet Personalized PageRank 2018-10-14	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
GOCN	Accuracy 84.8%	Robust Graph Data Learning via Latent Graph Convolutional Representation 2019-04-26	연결 없음
Graph U-Nets	Accuracy 84.4% ± 0.6%	Graph U-Nets 2019-05-11	코드 1 · 코드 2 · 코드 3
GResNet(GCN)	Accuracy 84.3%	GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation 2019-09-12	코드 1 · 코드 2