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
GloGNN	Accuracy 87.06±3.53	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
WRGAT	Accuracy 86.98 ± 3.78	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
LW-GCN	Accuracy 86.9±2.2	Label-Wise Graph Convolutional Network for Heterophilic Graphs 2021-10-15	코드 1
GGCN	Accuracy 86.86 ± 3.29	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
HLP Concat	Accuracy 86.67±4.22	Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs 2021-06-24	연결 없음
CNMPGNN	Accuracy 86.63 ± 3.57	CN-Motifs Perceptive Graph Neural Networks 2021-11-15	연결 없음
ACM-SGC-1	Accuracy 86.47 ± 3.77	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-SGC-2	Accuracy 86.47 ± 3.77	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
FDGATII	Accuracy 86.2745	FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping 2021-10-21	코드 1
MbaGCN	Accuracy 86.27±2.16	Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space 2025-01-26	연결 없음
HiGNN	Accuracy 85.88 ± 3.18	Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network 2024-03-26	코드 1
CATv3-sup	Accuracy 85.6±2.1	CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph 2023-12-14	코드 1
TDGNN-w	Accuracy 85.57 ± 3.78 (0, 3-5)	Tree Decomposed Graph Neural Network 2021-08-25	코드 1
H2GCN DHGR	Accuracy 85.01±5.51	Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach 2022-09-17	연결 없음
H2GCN-1	Accuracy 84.31 ± 3.70	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
Graph ESN	Accuracy 83.3±3.8	Beyond Homophily with Graph Echo State Networks 2022-10-27	연결 없음
H2GCN-2	Accuracy 83.14 ± 4.26	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
GPRGCN	Accuracy 82.55 ± 6.23	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
UGT	Accuracy 81.6 ±8.24	Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity 2023-08-18	코드 1 · 코드 2 · 코드 3
ADPA	Accuracy 81.6±3.5	Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification 2023-12-07	연결 없음
GCNII	Accuracy 80.39 ± 3.40	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
DeltaGNN linear	Accuracy 80.00±0.88	DeltaGNN: Graph Neural Network with Information Flow Control 2025-01-10	코드 1
FAGCN	Accuracy 79.61 ± 1.58	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
CT-Layer	Accuracy 79.05	DiffWire: Inductive Graph Rewiring via the Lovász Bound 2022-06-15	코드 1 · 코드 2
MixHop	Accuracy 75.88 ± 4.90	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
LINKX	Accuracy 75.49 ± 5.72	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
CT-Layer (PE)	Accuracy 69.25	DiffWire: Inductive Graph Rewiring via the Lovász Bound 2022-06-15	코드 1 · 코드 2
Geom-GCN-P	Accuracy 64.12	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
NLGCN	Accuracy 60.2 ± 5.3	Non-Local Graph Neural Networks 2020-05-29	코드 1
Geom-GCN-I	Accuracy 58.24	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4