Papers with Code · CC BY-SA 4.0

Cornell

아카이브 평가 60건 · 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
TDGNN-w	Accuracy 82.92 ± 6.61 (0, 2-6)	Tree Decomposed Graph Neural Network 2021-08-25	코드 1
ADPA	Accuracy 82.9±3.0	Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification 2023-12-07	연결 없음
GraphSAGE+DHGR	Accuracy 82.88±5.56	Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach 2022-09-17	연결 없음
FDGATII	Accuracy 82.4324	FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping 2021-10-21	코드 1
ACM-SGC-1	Accuracy 82.43 ± 5.44	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-SGC-2	Accuracy 82.43 ± 5.44	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
CNMPGNN	Accuracy 82.38 ± 6.13	CN-Motifs Perceptive Graph Neural Networks 2021-11-15	연결 없음
WRGAT	Accuracy 81.62 ± 3.90	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns 2021-06-11	코드 1
Graph ESN	Accuracy 81.1±6.0	Beyond Homophily with Graph Echo State Networks 2022-10-27	연결 없음
HiGNN	Accuracy 80.00 ± 4.26	Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network 2024-03-26	코드 1
H2GCN-2	Accuracy 79.46 ± 4.80	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 78.11 ± 6.68	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs 2020-06-20	코드 1 · 코드 2 · 코드 3 · 코드 4
GPRGCN	Accuracy 78.11 ± 6.55	Adaptive Universal Generalized PageRank Graph Neural Network 2020-06-14	코드 1
GCNII	Accuracy 77.86 ± 3.79	Simple and Deep Graph Convolutional Networks 2020-07-04	코드 1 · 코드 2 · 코드 3 · 코드 4
LINKX	Accuracy 77.84 ± 5.81	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
FAGCN	Accuracy 76.76 ± 5.87	Beyond Low-frequency Information in Graph Convolutional Networks 2021-01-04	코드 1
DeltaGNN - control + DC	Accuracy 75.67±1.91	DeltaGNN: Graph Neural Network with Information Flow Control 2025-01-10	코드 1
MixHop	Accuracy 73.51 ± 6.34	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing 2019-04-30	코드 1 · 코드 2 · 코드 3
UGT	Accuracy 70.0 ±4.44	Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity 2023-08-18	코드 1 · 코드 2 · 코드 3
CT-Layer	Accuracy 69.04	DiffWire: Inductive Graph Rewiring via the Lovász Bound 2022-06-15	코드 1 · 코드 2
GREET+CausalMP	Accuracy 68.23±2.90	Heterophilic Graph Neural Networks Optimization with Causal Message-passing 2024-11-21	연결 없음
MGNN + Hetero-S (4 layers)	Accuracy 68.18	The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs 2024-06-18	코드 1
Geom-GCN-P	Accuracy 60.81	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
CT-Layer (PE)	Accuracy 58.02	DiffWire: Inductive Graph Rewiring via the Lovász Bound 2022-06-15	코드 1 · 코드 2
NLGCN	Accuracy 57.6 ± 5.5	Non-Local Graph Neural Networks 2020-05-29	코드 1
Geom-GCN-I	Accuracy 56.76	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
Geom-GCN-S	Accuracy 55.68	Geom-GCN: Geometric Graph Convolutional Networks 2020-02-13	코드 1 · 코드 2 · 코드 3 · 코드 4
NLGAT	Accuracy 54.7 ± 7.6	Non-Local Graph Neural Networks 2020-05-29	코드 1
SDRF	Accuracy 54.60±0.39	Understanding over-squashing and bottlenecks on graphs via curvature 2021-11-29	코드 1 · 코드 2 · 코드 3 · 코드 4
PathNet	Accuracy (%) 91.35	Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network 2022-07-20	코드 1