Papers with Code · CC BY-SA 4.0

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아카이브 평가 62건 · 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.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
NCSAGE	Accuracy 43.89 ± 1.33	Clarify Confused Nodes via Separated Learning 2023-06-04	코드 1
NCGCN	Accuracy 43.16 ± 1.32	Clarify Confused Nodes via Separated Learning 2023-06-04	코드 1
2-HiGCN	Accuracy 41.81±0.52	Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes 2023-09-22	코드 1
IIE-GNN	Accuracy 39.91 ± 2.41	Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach 2022-11-20	연결 없음
LHS	Accuracy 38.87±1.0	Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks 2023-12-27	연결 없음
RDGNN-I	Accuracy 38.69 ± 1.41	Graph Neural Reaction Diffusion Models 2024-06-16	연결 없음
SignGT	Accuracy 38.65±0.32	SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning 2023-10-17	연결 없음
CATv3-sup	Accuracy 38.5±1.2	CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph 2023-12-14	코드 1
Ordered GNN	Accuracy 37.99 ± 1.00	Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing 2023-02-03	코드 1
MbaGCN	Accuracy 37.97±0.91	Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space 2025-01-26	연결 없음
GNNMoE(SAGE-like P)	Accuracy 37.97±1.01	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification 2024-12-11	코드 1
SADE-GCN	Accuracy 37.91 ± 0.97	Self-attention Dual Embedding for Graphs with Heterophily 2023-05-28	연결 없음
NLMLP	Accuracy 37.9 ± 1.3	Non-Local Graph Neural Networks 2020-05-29	코드 1
O(d)-NSD	Accuracy 37.81 ± 1.15	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Gen-NSD	Accuracy 37.80 ± 1.22	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Diag-NSD	Accuracy 37.79 ± 1.01	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
GNNMoE(GAT-like P)	Accuracy 37.76±0.98	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification 2024-12-11	코드 1
GloGNN++	Accuracy 37.7 ± 1.40	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
GGCN + UniGAP	Accuracy 37.69 ± 1.2	UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks 2024-07-28	코드 1
GNNMoE(GCN-like P)	Accuracy 37.59±1.36	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification 2024-12-11	코드 1
GGCN	Accuracy 37.54 ± 1.56	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
TE-GCNN	Accuracy 37.50±1.57	Transfer Entropy in Graph Convolutional Neural Networks 2024-06-08	코드 1
GCNII	Accuracy 37.44 ± 1.30	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
GPRGNN+DHGR	Accuracy 37.43 ± 0.78	Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach 2022-09-17	연결 없음
GloGNN	Accuracy 37.35 ± 1.30	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
ACM-GCN++	Accuracy 37.31 ± 1.09	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
HDP	Accuracy 37.26 ± 0.67	Heterophilous Distribution Propagation for Graph Neural Networks 2024-05-31	연결 없음
HiGNN	Accuracy 37.21 ± 1.35	Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network 2024-03-26	코드 1
ACMII-GCN++	Accuracy 37.09 ± 1.32	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
DJ-GNN	Accuracy 36.93 ± 0.84	Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters 2023-06-29	코드 1