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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.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
RDGNN-S	Accuracy 94.59 ± 5.97	Graph Neural Reaction Diffusion Models 2024-06-16	연결 없음
RDGNN-I	Accuracy 93.51 ± 5.93	Graph Neural Reaction Diffusion Models 2024-06-16	연결 없음
MGNN + Hetero-S (8 layers)	Accuracy 93.09	The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs 2024-06-18	코드 1
2-HiGCN	Accuracy 92.45±0.73	Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes 2023-09-22	코드 1
DJ-GNN	Accuracy 92.43±3.15	Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters 2023-06-29	코드 1
M2M-GNN	Accuracy 89.19 ± 4.5	Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs 2024-05-31	코드 1
ACM-GCN+	Accuracy 88.38 ± 3.64	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN++	Accuracy 88.38 ± 3.43	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACMII-GCN++	Accuracy 88.38 ± 3.43	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GRADE-GAT	Accuracy 88.3±3.5	Graph Neural Aggregation-diffusion with Metastability 2024-03-29	연결 없음
ACMII-GCN+	Accuracy 88.11 ± 3.24	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
ACM-GCN	Accuracy 87.84 ± 4.4	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
GCNH	Accuracy 87.84±3.87	GCNH: A Simple Method For Representation Learning On Heterophilous Graphs 2023-04-21	코드 1
HLP Concat	Accuracy 87.57 ± 5.44	Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs 2021-06-24	연결 없음
FSGNN	Accuracy 87.30 ± 5.55	Improving Graph Neural Networks with Simple Architecture Design 2021-05-17	코드 1
ACMII-GCN	Accuracy 86.76 ± 4.75	Revisiting Heterophily For Graph Neural Networks 2022-10-14	코드 1
H2GCN-RARE (λ=1.0)	Accuracy 86.76±5.80	GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy 2023-12-15	연결 없음
UGT	Accuracy 86.67 ±8.31	Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity 2023-08-18	코드 1 · 코드 2 · 코드 3
GraphSAGE + UniGAP	Accuracy 86.52 ± 4.8	UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks 2024-07-28	코드 1
SADE-GCN	Accuracy 86.49±5.12	Self-attention Dual Embedding for Graphs with Heterophily 2023-05-28	연결 없음
LHS	Accuracy 86.32±4.5	Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks 2023-12-27	연결 없음
Ordered GNN	Accuracy 86.22±4.12	Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing 2023-02-03	코드 1
HiGNN	Accuracy 86.22 ± 4.67	Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network 2024-03-26	코드 1
Conn-NSD	Accuracy 86.16±2.24	Sheaf Neural Networks with Connection Laplacians 2022-06-17	코드 1
O(d)-NSD	Accuracy 85.95 ± 5.51	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
IIE-GNN	Accuracy 85.84±4.23	Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach 2022-11-20	연결 없음
CNMPGNN	Accuracy 85.68±5.28	CN-Motifs Perceptive Graph Neural Networks 2021-11-15	연결 없음
Diag-NSD	Accuracy 85.67 ± 6.95	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs 2022-02-09	코드 1
NLMLP	Accuracy 85.4 ± 3.8	Non-Local Graph Neural Networks 2020-05-29	코드 1
UniG-Encoder	Accuracy 85.40±5.3	UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification 2023-08-03	코드 1