Film(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
NLMLP	1:1 Accuracy 37.9 ± 1.3	Non-Local Graph Neural Networks 2020-05-29	코드 1
O(d)-NSD	1:1 Accuracy 37.81 ± 1.15	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Gen-NSD	1:1 Accuracy 37.80 ± 1.22	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Diag-NSD	1:1 Accuracy 37.79 ± 1.01	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
GloGNN++	1:1 Accuracy 37.70 ± 1.40	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
GGCN	1:1 Accuracy 37.54 ± 1.56	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
GCNII	1:1 Accuracy 37.44 ± 1.30	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
GloGNN	1:1 Accuracy 37.35 ± 1.30	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
ACM-GCN++	1:1 Accuracy 37.31 ± 1.09	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACMII-GCN++	1:1 Accuracy 37.09 ± 1.32	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
Deformable GCN	1:1 Accuracy 37.07±0.79	Deformable Graph Convolutional Networks 2021-12-29	코드 1
ACM-GCN	1:1 Accuracy 36.63 ± 0.84	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
WRGAT	1:1 Accuracy 36.53 ± 0.77	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns 2021-06-11	코드 1
ACMII-GCN	1:1 Accuracy 36.31 ± 1.2	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN+	1:1 Accuracy 36.26 ± 1.34	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACMII-GCN+	1:1 Accuracy 36.14 ± 1.44	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
LINKX	1:1 Accuracy 36.10 ± 1.55	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
ACM-SGC-2	1:1 Accuracy 36.04 ± 0.83	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
H2GCN	1:1 Accuracy 35.70 ± 1.00	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 35.49 ± 1.06	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GPRGCN	1:1 Accuracy 35.16 ± 0.9	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
FAGCN	1:1 Accuracy 34.82 ± 1.35	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
MixHop	1:1 Accuracy 32.22 ± 2.34	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
NLGCN	1:1 Accuracy 31.6 ± 1.0	Non-Local Graph Neural Networks 2020-05-29	코드 1
Geom-GCN	1:1 Accuracy 31.59 ± 1.15	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
NLGAT	1:1 Accuracy 29.5 ± 1.3	Non-Local Graph Neural Networks 2020-05-29	코드 1