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
RDGNN-I	Accuracy 92.72 ± 5.88	Graph Neural Reaction Diffusion Models 2024-06-16	연결 없음
CATv3-sup	Accuracy 88.8±2.1	CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph 2023-12-14	코드 1
FSGNN (8-hop)	Accuracy 87.84±6.19	Improving Graph Neural Networks with Simple Architecture Design 2021-05-17	코드 1
H2GCN-RARE (λ=1.0)	Accuracy 87.84±4.05	GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy 2023-12-15	연결 없음
Ordered GNN	Accuracy 87.03±4.73	Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing 2023-02-03	코드 1
DJ-GNN	Accuracy 87.03±1.62	Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters 2023-06-29	코드 1
UniG-Encoder	Accuracy 86.75±6.56	UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification 2023-08-03	코드 1
Diag-NSD	Accuracy 86.49 ± 7.35	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
ACMII-GCN++	Accuracy 86.49 ± 6.73	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GCNH	Accuracy 86.49±6.98	GCNH: A Simple Method For Representation Learning On Heterophilous Graphs 2023-04-21	코드 1
M2M-GNN	Accuracy 86.48 ± 6.1	Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs 2024-05-31	코드 1
SADE-GCN	Accuracy 86.21±5.59	Self-attention Dual Embedding for Graphs with Heterophily 2023-05-28	연결 없음
LHS	Accuracy 85.96±5.1	Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks 2023-12-27	연결 없음
GloGNN++	Accuracy 85.95±5.10	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
Conn-NSD	Accuracy 85.95±7.72	Sheaf Neural Networks with Connection Laplacians 2022-06-17	코드 1
ACMII-GCN	Accuracy 85.95 ± 5.64	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GGCN	Accuracy 85.68 ± 6.63	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks 2021-02-12	코드 1
Gen-NSD	Accuracy 85.68 ± 6.51	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
ACM-GCN+	Accuracy 85.68 ± 4.84	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN++	Accuracy 85.68 ± 5.8	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
TE-GCNN	Accuracy 85.68 ± 6.63	Transfer Entropy in Graph Convolutional Neural Networks 2024-06-08	코드 1
ACMII-GCN+	Accuracy 85.41 ± 5.3	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN	Accuracy 85.14 ± 6.07	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
H2GCN + UniGAP	Accuracy 84.96 ± 5.0	UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks 2024-07-28	코드 1
NLMLP	Accuracy 84.9 ± 5.7	Non-Local Graph Neural Networks 2020-05-29	코드 1
O(d)-NSD	Accuracy 84.86 ± 4.71	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
UDGNN (GCN)	Accuracy 84.32±7.29	Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing 2022-05-30	연결 없음
HLP Concat	Accuracy 84.05±4.67	Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs 2021-06-24	연결 없음
GloGNN	Accuracy 83.51±4.26	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily 2022-05-15	코드 1
GRADE-GAT	Accuracy 83.3±7.0	Graph Neural Aggregation-diffusion with Metastability 2024-03-29	연결 없음