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.

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원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
GCNH	Accuracy 36.89 ± 1.50	GCNH: A Simple Method For Representation Learning On Heterophilous Graphs 2023-04-21	코드 1
M2M-GNN	Accuracy 36.72 ± 1.6	Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs 2024-05-31	코드 1
ACM-GCN	Accuracy 36.63 ± 0.84	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
WRGAT	Accuracy 36.53 ± 0.77	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns 2021-06-11	코드 1
ACMII-GCN	Accuracy 36.31 ± 1.2	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN+	Accuracy 36.26 ± 1.34	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
CNMPGNN	Accuracy 36.25 ± 0.98	CN-Motifs Perceptive Graph Neural Networks 2021-11-15	연결 없음
LSC-GNN	Accuracy 36.2 ± 1.0	Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering 2022-06-06	연결 없음
ACMII-GCN+	Accuracy 36.14 ± 1.44	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
UDGNN (GCN)	Accuracy 36.13 ± 1.21	Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing 2022-05-30	연결 없음
LINKX	Accuracy 36.10 ± 1.55	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
ACM-SGC-2	Accuracy 36.04 ± 0.83	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
MGNN + Hetero-S (4 layers)	Accuracy 35.99	The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs 2024-06-18	코드 1
ChebNet+Bregman	Accuracy 35.92 ± 0.84	Bregman Graph Neural Network 2023-09-12	코드 1
FSGNN (8-hop)	Accuracy 35.75 ± 0.96	Improving Graph Neural Networks with Simple Architecture Design 2021-05-17	코드 1
ACM-SGC-1	Accuracy 35.49 ± 1.06	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GPRGCN	Accuracy 35.16 ± 0.9	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
FAGCN	Accuracy 34.82 ± 1.35	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
HLP Concat	Accuracy 34.59 ± 1.32	Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs 2021-06-24	연결 없음
GESN	Accuracy 34.56 ± 0.76	Addressing Heterophily in Node Classification with Graph Echo State Networks 2023-05-14	코드 1
Graph ESN	Accuracy 34.5 ± 0.8	Beyond Homophily with Graph Echo State Networks 2022-10-27	연결 없음
H2GCN-2	Accuracy 34.49 ± 1.63	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 34.31 ± 1.31	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
MixHop	Accuracy 32.22 ± 2.34	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
CT-Layer	Accuracy 31.98	DiffWire: Inductive Graph Rewiring via the Lovász Bound 2022-06-15	코드 1 · 코드 2
Geom-GCN-P	Accuracy 31.63	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
NLGCN	Accuracy 31.6 ± 1.0	Non-Local Graph Neural Networks 2020-05-29	코드 1
Geom-GCN-S	Accuracy 30.3	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
NLGAT	Accuracy 29.5 ± 1.3	Non-Local Graph Neural Networks 2020-05-29	코드 1
CT-Layer (PE)	Accuracy 29.35	DiffWire: Inductive Graph Rewiring via the Lovász Bound 2022-06-15	코드 1 · 코드 2