Papers with Code · CC BY-SA 4.0

NCI1

아카이브 평가 69건 · 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
GATv2	Accuracy 82.384±1.700	How Attentive are Graph Attention Networks? 2021-05-30	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8
GAT-GC (f-Scaled)	Accuracy 82.28%	Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation 2019-07-04	코드 1
DAGCN	Accuracy 81.68%	DAGCN: Dual Attention Graph Convolutional Networks 2019-04-04	코드 1
NERO	Accuracy 81.63%	Relation order histograms as a network embedding tool 2021-06-09	코드 1
GFN-light	Accuracy 81.43%	Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification 2019-05-11	코드 1
VRGC	Accuracy 80.7%	Variational Recurrent Neural Networks for Graph Classification 2019-02-07	코드 1
GIUNet	Accuracy 80.2%	Graph isomorphism UNet 2023-08-23	코드 1
GIN	Accuracy 80%	A Fair Comparison of Graph Neural Networks for Graph Classification 2019-12-20	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
S-CGIB	Accuracy 79.75±0.82	Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck 2025-02-20	코드 1
HGP-SL	Accuracy 78.45%	Hierarchical Graph Pooling with Structure Learning 2019-11-14	코드 1 · 코드 2 · 코드 3
CapsGNN	Accuracy 78.35%	Capsule Graph Neural Network 2019-05-01	코드 1
UGT	Accuracy 77.55 ±0.16%	Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity 2023-08-18	코드 1 · 코드 2 · 코드 3
Local Topological Profile (LTP)	Accuracy 77.1 ± 3.7 Accuracy (10-fold) 77.1 ± 3.7	Strengthening structural baselines for graph classification using Local Topological Profile 2023-05-01	코드 1
Graphormer	Accuracy 77.032±1.393	Do Transformers Really Perform Bad for Graph Representation? 2021-06-09	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
WEGL	Accuracy 76.8%	Wasserstein Embedding for Graph Learning 2020-06-16	코드 1
TokenGT	Accuracy 76.740±2.054	Pure Transformers are Powerful Graph Learners 2022-07-06	코드 1 · 코드 2
DGCNN	Accuracy 76.4%	A Fair Comparison of Graph Neural Networks for Graph Classification 2019-12-20	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
PSCN	Accuracy 76.34%	Learning Convolutional Neural Networks for Graphs 2016-05-17	코드 1 · 코드 2
k-GNN	Accuracy 76.2%	Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks 2018-10-04	코드 1
TREE-G	Accuracy 75.9%	TREE-G: Decision Trees Contesting Graph Neural Networks 2022-07-06	코드 1
SF + RFC	Accuracy 75.2%	A Simple Baseline Algorithm for Graph Classification 2018-10-22	코드 1 · 코드 2 · 코드 3
Fea2Fea-s3	Accuracy 74.9%	Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks 2021-06-24	코드 1
SAEPool_g	Accuracy 74.48%	Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization 2022-04-06	연결 없음
SAGPool_g	Accuracy 74.06%	Self-Attention Graph Pooling 2019-04-17	코드 1 · 코드 2 · 코드 3
NDP	Accuracy 73.5%	Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling 2019-10-24	코드 1
2-WL-GNN	Accuracy 73.5	A Novel Higher-order Weisfeiler-Lehman Graph Convolution 2020-07-01	코드 1
graph2vec	Accuracy 73.22% ± 1.81%	graph2vec: Learning Distributed Representations of Graphs 2017-07-17	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6
LDP	Accuracy 73.0%	A simple yet effective baseline for non-attributed graph classification 2018-11-08	코드 1 · 코드 2
FGW raw sp	Accuracy 72.82%	Optimal Transport for structured data with application on graphs 2018-05-23	코드 1 · 코드 2
MAGPool	Accuracy 72.32%	Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective 2024-03-04	코드 1