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아카이브 평가 26건 · 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.

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원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
GCNII	1:1 Accuracy 90.15 ± 0.43	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
Geom-GCN	1:1 Accuracy 89.95 ± 0.47	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
ACMII-GCN	1:1 Accuracy 89.89 ± 0.43	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN+	1:1 Accuracy 89.82 ± 0.41	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACMII-GCN+	1:1 Accuracy 89.78 ± 0.49	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACMII-GCN++	1:1 Accuracy 89.71 ± 0.48	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN++	1:1 Accuracy 89.65 ± 0.58	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GloGNN	1:1 Accuracy 89.62 ± 0.35	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
H2GCN	1:1 Accuracy 89.49 ± 0.38	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
O(d)-NSD	1:1 Accuracy 89.49 ± 0.40	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Diag-NSD	1:1 Accuracy 89.42 ± 0.43	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Gen-NSD	1:1 Accuracy 89.33 ± 0.35	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
GloGNN++	1:1 Accuracy 89.24 ± 0.39	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
GESN	1:1 Accuracy 89.20 ± 0.34	Addressing Heterophily in Node Classification with Graph Echo State Networks 2023-05-14	코드 1
GGCN	1:1 Accuracy 89.15 ± 0.37	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
ACM-SGC-2	1:1 Accuracy 89.01 ± 0.6	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
NLGCN	1:1 Accuracy 89.0 ± 0.5	Non-Local Graph Neural Networks 2020-05-29	코드 1
WRGAT	1:1 Accuracy 88.52 ± 0.92	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns 2021-06-11	코드 1
ACM-SGC-1	1:1 Accuracy 88.49 ± 0.51	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
NLMLP	1:1 Accuracy 88.2 ± 0.5	Non-Local Graph Neural Networks 2020-05-29	코드 1
NLGAT	1:1 Accuracy 88.2 ± 0.3	Non-Local Graph Neural Networks 2020-05-29	코드 1
FAGCN	1:1 Accuracy 88.09 ± 1.38	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
LINKX	1:1 Accuracy 87.86 ± 0.77	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
GPRGCN	1:1 Accuracy 87.54 ± 0.38	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
MixHop	1:1 Accuracy 85.31 ± 0.61	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
GREAD-BS	Accuracy 90.21	GREAD: Graph Neural Reaction-Diffusion Networks 2022-11-25	코드 1