Papers with Code · CC BY-SA 4.0

Wisconsin

아카이브 평가 63건 · 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
5-HiGCN	Accuracy 94.99±0.65	Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes 2023-09-22	코드 1
RDGNN-I	Accuracy 93.72 ± 4.59	Graph Neural Reaction Diffusion Models 2024-06-16	연결 없음
DJ-GNN	Accuracy 92.54±3.70	Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters 2023-06-29	코드 1
H2GCN-RARE (λ=1.0)	Accuracy 90.00±2.97	GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy 2023-12-15	연결 없음
O(d)-NSD	Accuracy 89.41 ± 4.74	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
Gen-NSD	Accuracy 89.21 ± 3.84	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
M2M-GNN	Accuracy 89.01 ± 4.1	Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs 2024-05-31	코드 1
HDP	Accuracy 88.82 ± 3.40	Heterophilous Distribution Propagation for Graph Neural Networks 2024-05-31	연결 없음
MGNN + Hetero-S (6 layers)	Accuracy 88.77	The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs 2024-06-18	코드 1
Conn-NSD	Accuracy 88.73±4.47	Sheaf Neural Networks with Connection Laplacians 2022-06-17	코드 1
Diag-NSD	Accuracy 88.63 ± 2.75	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
SADE-GCN	Accuracy 88.63±4.54	Self-attention Dual Embedding for Graphs with Heterophily 2023-05-28	연결 없음
FSGNN (3-hop)	Accuracy 88.43±3.22	Improving Graph Neural Networks with Simple Architecture Design 2021-05-17	코드 1
ACM-GCN	Accuracy 88.43 ± 3.22	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN+	Accuracy 88.43 ± 2.39	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACMII-GCN++	Accuracy 88.43 ± 3.66	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
LHS	Accuracy 88.32±2.3	Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks 2023-12-27	연결 없음
ACM-GCN++	Accuracy 88.24 ± 3.16	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GloGNN++	Accuracy 88.04±3.22	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
ACMII-GCN+	Accuracy 88.04 ± 3.66	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
Ordered GNN	Accuracy 88.04±3.63	Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing 2023-02-03	코드 1
UniG-Encoder	Accuracy 88.03±4.42	UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification 2023-08-03	코드 1
CoED	Accuracy 87.84±3.70	Improving Graph Neural Networks by Learning Continuous Edge Directions 2024-10-18	코드 1
H2GCN + UniGAP	Accuracy 87.73 ± 4.8	UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks 2024-07-28	코드 1
GRADE-GAT	Accuracy 87.7±3.7	Graph Neural Aggregation-diffusion with Metastability 2024-03-29	연결 없음
GCNH	Accuracy 87.65±3.59	GCNH: A Simple Method For Representation Learning On Heterophilous Graphs 2023-04-21	코드 1
UDGNN (GCN)	Accuracy 87.64±3.74	Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing 2022-05-30	연결 없음
ACMII-GCN	Accuracy 87.45 ± 3.74	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
TE-GCNN	Accuracy 87.45 ± 3.70	Transfer Entropy in Graph Convolutional Neural Networks 2024-06-08	코드 1
NLMLP	Accuracy 87.3 ± 4.3	Non-Local Graph Neural Networks 2020-05-29	코드 1