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아카이브 평가 62건 · 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
GGCN	Accuracy 84.86 ± 4.55	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
H2GCN+DHGR	Accuracy 84.86±5.01	Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach 2022-09-17	연결 없음
TE-GCNN	Accuracy 84.86 ± 4.55	Transfer Entropy in Graph Convolutional Neural Networks 2024-06-08	코드 1
UDGNN (GCN)	Accuracy 84.60±5.32	Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing 2022-05-30	연결 없음
CoED	Accuracy 84.59±4.53	Improving Graph Neural Networks by Learning Continuous Edge Directions 2024-10-18	코드 1
GloGNN	Accuracy 84.32±4.15	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
Graph ESN	Accuracy 84.3±4.4	Beyond Homophily with Graph Echo State Networks 2022-10-27	연결 없음
GloGNN++	Accuracy 84.05±4.90	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
ChebNet+Bregman	Accuracy 84.05 ± 5.47	Bregman Graph Neural Network 2023-09-12	코드 1
ADPA	Accuracy 83.8±2.7	Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification 2023-12-07	연결 없음
WRGAT	Accuracy 83.62 ± 5.50	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns 2021-06-11	코드 1
H2GCN-1	Accuracy 83.24 ± 7.07	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
TDGNN-w	Accuracy 83.00 ± 4.50	Tree Decomposed Graph Neural Network 2021-08-25	코드 1
CATv3-sup	Accuracy 83.0±2.5	CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph 2023-12-14	코드 1
Gen-NSD	Accuracy 82.97 ± 5.13	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
ACM-SGC-1	Accuracy 81.89 ± 4.53	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-SGC-2	Accuracy 81.89 ± 4.53	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GPRGCN	Accuracy 81.35 ± 5.32	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
FDGATII	Accuracy 80.5405	FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping 2021-10-21	코드 1
H2GCN-2	Accuracy 80.00 ± 6.77	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
MixHop	Accuracy 77.84 ± 7.73	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
GCNII	Accuracy 77.57 ± 3.83	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
FAGCN	Accuracy 76.49 ± 2.87	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
LINKX	Accuracy 74.60 ± 8.37	Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods 2021-10-27	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
DeltaGNN constant	Accuracy 74.05±3.08	DeltaGNN: Graph Neural Network with Information Flow Control 2025-01-10	코드 1
Geom-GCN-P	Accuracy 67.57	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
NLGCN	Accuracy 65.5 ± 6.6	Non-Local Graph Neural Networks 2020-05-29	코드 1
SDRF	Accuracy 64.46±0.38	Understanding over-squashing and bottlenecks on graphs via curvature 2021-11-29	코드 1 · 코드 2 · 코드 3 · 코드 4
NLGAT	Accuracy 62.6 ± 7.1	Non-Local Graph Neural Networks 2020-05-29	코드 1
Geom-GCN-S	Accuracy 59.73	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4