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

아카이브 평가 27건 · 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
Diag-NSD	1:1 Accuracy 86.49 ± 7.35	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
ACMII-GCN++	1:1 Accuracy 86.49 ± 6.73	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GloGNN++	1:1 Accuracy 85.95 ± 5.10	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
ACMII-GCN	1:1 Accuracy 85.95 ± 5.64	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
Deformable GCN	1:1 Accuracy 85.95±4.37	Deformable Graph Convolutional Networks 2021-12-29	코드 1
GGCN	1:1 Accuracy 85.68 ± 6.63	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
Gen-NSD	1:1 Accuracy 85.68 ± 6.51	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
ACM-GCN+	1:1 Accuracy 85.68 ± 4.84	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN++	1:1 Accuracy 85.68 ± 5.8	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACMII-GCN+	1:1 Accuracy 85.41 ± 5.3	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN	1:1 Accuracy 85.14 ± 6.07	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
NLMLP	1:1 Accuracy 84.9 ± 5.7	Non-Local Graph Neural Networks 2020-05-29	코드 1
O(d) - NSD	1:1 Accuracy 84.86 ± 4.71	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
GloGNN	1:1 Accuracy 83.51 ± 4.26	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
H2GCN	1:1 Accuracy 82.70 ± 5.28	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
ACM-SGC-1	1:1 Accuracy 82.43 ± 5.44	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-SGC-2	1:1 Accuracy 82.43 ± 5.44	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
WRGAT	1:1 Accuracy 81.62 ±3.90	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns 2021-06-11	코드 1
GESN	1:1 Accuracy 81.14 ± 6.00	Addressing Heterophily in Node Classification with Graph Echo State Networks 2023-05-14	코드 1
GPRGCN	1:1 Accuracy 78.11 ± 6.55	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
GCNII	1:1 Accuracy 77.86 ± 3.79	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
LINKX	1:1 Accuracy 77.84 ± 5.81	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
FAGCN	1:1 Accuracy 76.76 ± 5.87	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
MixHop	1:1 Accuracy 73.51 ± 6.34	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
Geom-GCN	1:1 Accuracy 60.54 ± 3.67	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
NLGCN	1:1 Accuracy 57.6 ± 5.5	Non-Local Graph Neural Networks 2020-05-29	코드 1
NLGAT	1:1 Accuracy 54.7 ± 7.6	Non-Local Graph Neural Networks 2020-05-29	코드 1