Wisconsin (48%/32%/20% fixed splits)

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
O(d)-NSD	1:1 Accuracy 89.41 ± 4.74	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Gen-NSD	1:1 Accuracy 89.21 ± 3.84	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Diag-NSD	1:1 Accuracy 88.63 ± 2.75	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
ACM-GCN	1:1 Accuracy 88.43 ± 3.22	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN+	1:1 Accuracy 88.43 ± 2.39	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACMII-GCN++	1:1 Accuracy 88.43 ± 3.66	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN++	1:1 Accuracy 88.24 ± 3.16	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GloGNN++	1:1 Accuracy 88.04 ± 3.22	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
ACMII-GCN+	1:1 Accuracy 88.04 ± 3.66	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
H2GCN	1:1 Accuracy 87.65 ± 4.98	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
ACMII-GCN	1:1 Accuracy 87.45 ± 3.74	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
NLMLP	1:1 Accuracy 87.3 ± 4.3	Non-Local Graph Neural Networks 2020-05-29	코드 1
GloGNN	1:1 Accuracy 87.06 ± 3.53	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
WRGAT	1:1 Accuracy 86.98 ± 3.78	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns 2021-06-11	코드 1
GGCN	1:1 Accuracy 86.86 ± 3.29	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
ACM-SGC-1	1:1 Accuracy 86.47 ± 3.77	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-SGC-2	1:1 Accuracy 86.47 ± 3.77	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GESN	1:1 Accuracy 83.33 ± 3.81	Addressing Heterophily in Node Classification with Graph Echo State Networks 2023-05-14	코드 1
GPRGCN	1:1 Accuracy 82.55 ± 6.23	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
GCNII	1:1 Accuracy 80.39 ± 3.40	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
FAGCN	1:1 Accuracy 79.61 ± 1.58	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
MixHop	1:1 Accuracy 75.88 ± 4.90	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
LINKX	1:1 Accuracy 75.49 ± 5.72	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
Geom-GCN	1:1 Accuracy 64.51 ± 3.66	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
NLGCN	1:1 Accuracy 60.2 ± 5.3	Non-Local Graph Neural Networks 2020-05-29	코드 1
NLGAT	1:1 Accuracy 56.9 ± 7.3	Non-Local Graph Neural Networks 2020-05-29	코드 1