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

아카이브 평가 29건 · 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
ScaleNet	1:1 Accuracy 76.0±2.0	Scale Invariance of Graph Neural Networks 2024-11-28	코드 1
Dir-GNN	1:1 Accuracy 75.31±1.92	Edge Directionality Improves Learning on Heterophilic Graphs 2023-05-17	코드 1
GESN	1:1 Accuracy 73.56 ± 1.62	Addressing Heterophily in Node Classification with Graph Echo State Networks 2023-05-14	코드 1
ACMII-GCN++	1:1 Accuracy 67.4 ± 2.21	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACMII-GCN+	1:1 Accuracy 67.07 ± 1.65	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN++	1:1 Accuracy 67.06 ± 1.66	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN+	1:1 Accuracy 66.98 ± 1.71	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
Deformable GCN	1:1 Accuracy 62.56 ± 1.31	Deformable Graph Convolutional Networks 2021-12-29	코드 1
LINKX	1:1 Accuracy 61.81 ± 1.80	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
NLGCN	1:1 Accuracy 59.0 ± 1.2	Non-Local Graph Neural Networks 2020-05-29	코드 1
GloGNN++	1:1 Accuracy 57.88 ± 1.76	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
GloGNN	1:1 Accuracy 57.54 ± 1.39	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
NLGAT	1:1 Accuracy 56.8 ± 2.5	Non-Local Graph Neural Networks 2020-05-29	코드 1
O(d)-NSD	1:1 Accuracy 56.34 ± 1.32	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
ACM-GCN	1:1 Accuracy 55.19 ± 1.49	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GGCN	1:1 Accuracy 55.17 ± 1.58	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
Diag-NSD	1:1 Accuracy 54.78 ± 1.81	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Gen-NSD	1:1 Accuracy 53.17 ± 1.31	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
ACMII-GCN	1:1 Accuracy 51.8 ± 1.5	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
WRGAT	1:1 Accuracy 48.85 ± 0.78	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns 2021-06-11	코드 1
GPRGCN	1:1 Accuracy 46.31 ± 2.46	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
ACM-SGC-1	1:1 Accuracy 45.00 ± 1.4	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
MixHop	1:1 Accuracy 43.80 ± 1.48	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
ACM-SGC-2	1:1 Accuracy 40.02 ± 0.96	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GCNII	1:1 Accuracy 38.47 ± 1.58	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
Geom-GCN	1:1 Accuracy 38.15 ± 0.92	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
H2GCN	1:1 Accuracy 36.48 ± 1.86	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
NLMLP	1:1 Accuracy 33.7 ± 1.5	Non-Local Graph Neural Networks 2020-05-29	코드 1
FAGCN	1:1 Accuracy 30.83 ± 0.69	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1