Papers with Code · CC BY-SA 4.0

Squirrel

아카이브 평가 59건 · 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
IIE-GNN	Accuracy 57.32±1.89	Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach 2022-11-20	연결 없음
NLGAT	Accuracy 56.8 ± 2.5	Non-Local Graph Neural Networks 2020-05-29	코드 1
O(d)-NSD	Accuracy 56.34 ± 1.32	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
LCS-GAT	Accuracy 56.3 ± 2.2	Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering 2022-06-06	연결 없음
GCN-RARE (λ=1.0)	Accuracy 55.90±1.39	GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy 2023-12-15	연결 없음
ACM-GCN	Accuracy 55.19 ± 1.49	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GGCN	Accuracy 55.17 ± 1.58	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
TE-GCNN	Accuracy 55.04±1.64	Transfer Entropy in Graph Convolutional Neural Networks 2024-06-08	코드 1
Diag-NSD	Accuracy 54.78 ± 1.81	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
HiGNN	Accuracy 54.78 ± 1.58	Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network 2024-03-26	코드 1
Gen-NSD	Accuracy 53.17 ± 1.31	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
ACMII-GCN	Accuracy 51.8 ± 1.5	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
WRGAT	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	Accuracy 46.31 ± 2.46	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
ADPA	Accuracy 45.2±1.3	Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification 2023-12-07	연결 없음
Conn-NSD	Accuracy 45.19±1.57	Sheaf Neural Networks with Connection Laplacians 2022-06-17	코드 1
ACM-SGC-1	Accuracy 45.00 ± 1.4	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
MixHop	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	Accuracy 40.02 ± 0.96	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
Gprompt+CausalMP	Accuracy 39.78±0.91	Heterophilic Graph Neural Networks Optimization with Causal Message-passing 2024-11-21	연결 없음
GCNII	Accuracy 38.47 ± 1.58	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
Geom-GCN-P	Accuracy 38.14	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
SDRF	Accuracy 37.05±0.17	Understanding over-squashing and bottlenecks on graphs via curvature 2021-11-29	코드 1 · 코드 2 · 코드 3 · 코드 4
Geom-GCN-S	Accuracy 36.24	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
NLMLP	Accuracy 33.7 ± 1.5	Non-Local Graph Neural Networks 2020-05-29	코드 1
Geom-GCN-I	Accuracy 33.32	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
H2GCN-2	Accuracy 32.33 ± 1.94	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
FAGCN	Accuracy 30.83 ± 0.69	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
H2GCN-1	Accuracy 28.98 ± 1.97	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4