Papers with Code · CC BY-SA 4.0

Chameleon

아카이브 평가 61건 · 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
GloGNN	Accuracy 69.78±2.42	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
UGT	Accuracy 69.78 ±3.21	Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity 2023-08-18	코드 1 · 코드 2 · 코드 3
GraphSAGE-RARE (λ=1.0)	Accuracy 69.28±1.90	GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy 2023-12-15	연결 없음
ACM-GCN	Accuracy 69.14 ± 1.91	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
HiGNN	Accuracy 68.86 ± 1.45	Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network 2024-03-26	코드 1
Diag-NSD	Accuracy 68.68 ± 1.73	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
2-HiGCN	Accuracy 68.47±0.45	Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes 2023-09-22	코드 1
ACMII-GCN	Accuracy 68.46 ± 1.7	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
LINKX	Accuracy 68.42 ± 1.38	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
LSC-ARMA	Accuracy 68.4 ± 2.3	Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering 2022-06-06	연결 없음
O(d)-NSD	Accuracy 68.04 ± 1.58	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Gen-NSD	Accuracy 67.93 ± 1.58	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
NLGAT	Accuracy 65.7 ± 1.4	Non-Local Graph Neural Networks 2020-05-29	코드 1
WRGAT	Accuracy 65.24 ± 0.87	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns 2021-06-11	코드 1
Conn-NSD	Accuracy 65.21±2.04	Sheaf Neural Networks with Connection Laplacians 2022-06-17	코드 1
FDGATII	Accuracy 65.1754	FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping 2021-10-21	코드 1
ACM-SGC-1	Accuracy 63.99 ± 1.66	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GCNII	Accuracy 63.86 ± 3.04	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
GPRGCN	Accuracy 62.59 ± 2.04	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
Geom-GCN-P	Accuracy 60.9	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
MixHop	Accuracy 60.50 ± 2.53	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
Geom-GCN-I	Accuracy 60.31	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
Geom-GCN-S	Accuracy 59.96	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
ACM-SGC-2	Accuracy 59.21 ± 2.22	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
Gprompt+CausalMP	Accuracy 59.14±2.42	Heterophilic Graph Neural Networks Optimization with Causal Message-passing 2024-11-21	연결 없음
H2GCN-2	Accuracy 58.38 ± 1.76	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
H2GCN-1	Accuracy 52.96 ± 2.09	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
NLMLP	Accuracy 50.7 ± 2.2	Non-Local Graph Neural Networks 2020-05-29	코드 1
ADPA	Accuracy 46.2±1.3	Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification 2023-12-07	연결 없음
FAGCN	Accuracy 46.07 ± 2.11	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1