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MUTAG

아카이브 평가 74건 · 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
Evolution of Graph Classifiers	Accuracy 100.00% Accuracy (10-fold) 100	Evolution of Graph Classifiers 2019-10-04	코드 1
MEWISPool	Accuracy 96.66%	Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks 2021-07-03	코드 1
TFGW ADJ (L=2)	Accuracy 96.4%	Template based Graph Neural Network with Optimal Transport Distances 2022-05-31	코드 1
GIUNet	Accuracy 95.7%	Graph isomorphism UNet 2023-08-23	코드 1
G_Inception	Accuracy 95.00%	When Work Matters: Transforming Classical Network Structures to Graph CNN 2018-07-07	연결 없음
GIC	Accuracy 94.44%	Gaussian-Induced Convolution for Graphs 2018-11-11	연결 없음
CIN++	Accuracy 94.4%	CIN++: Enhancing Topological Message Passing 2023-06-06	코드 1
sGIN	Accuracy 94.14%	Mutual Information Maximization in Graph Neural Networks 2019-05-21	코드 1 · 코드 2
CAN	Accuracy 94.1%	Cell Attention Networks 2022-09-16	코드 1
Deep WL SGN(0,1,2)	Accuracy 93.68%	Subgraph Networks with Application to Structural Feature Space Expansion 2019-03-21	연결 없음
QS-CNNs (Quantum Walk)	Accuracy 93.13% Accuracy (10-fold) 93.13	Quantum-based subgraph convolutional neural networks 2019-04-01	연결 없음
PATCHY-SAN	Accuracy 92.63%	Learning Convolutional Neural Networks for Graphs 2016-05-17	코드 1 · 코드 2
DDGK	Accuracy 91.58%	DDGK: Learning Graph Representations for Deep Divergence Graph Kernels 2019-04-21	코드 1
Graph-JEPA	Accuracy 91.25%	Graph-level Representation Learning with Joint-Embedding Predictive Architectures 2023-09-27	코드 1
GraphStar	Accuracy 91.2%	Graph Star Net for Generalized Multi-Task Learning 2019-06-21	코드 1
TREE-G	Accuracy 91.1%	TREE-G: Decision Trees Contesting Graph Neural Networks 2022-07-06	코드 1
SEG-BERT	Accuracy 90.85%	Segmented Graph-Bert for Graph Instance Modeling 2020-02-09	코드 1
GFN	Accuracy 90.84%	Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification 2019-05-11	코드 1
PPGN	Accuracy 90.55%	Provably Powerful Graph Networks 2019-05-27	코드 1 · 코드 2
GAT-GC (f-Scaled)	Accuracy 90.44%	Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation 2019-07-04	코드 1
DropGIN	Accuracy 90.4%	DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks 2021-11-11	코드 1
LDP	Accuracy 90.1%	A simple yet effective baseline for non-attributed graph classification 2018-11-08	코드 1 · 코드 2
R-GCN + PANDA	Accuracy 90.05%	PANDA: Expanded Width-Aware Message Passing Beyond Rewiring 2024-06-06	코드 1
hGANet	Accuracy 90.00%	Graph Representation Learning via Hard and Channel-Wise Attention Networks 2019-07-05	코드 1
U2GNN	Accuracy 89.97%	Universal Graph Transformer Self-Attention Networks 2019-09-26	코드 1
FactorGCN	Accuracy 89.9% Accuracy (10-fold) 89.9%	Factorizable Graph Convolutional Networks 2020-10-12	코드 1
GFN-light	Accuracy 89.89%	Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification 2019-05-11	코드 1
GIN-0	Accuracy 89.4%	How Powerful are Graph Neural Networks? 2018-10-01	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 · 코드 10 · 코드 11 · 코드 12 · 코드 13 · 코드 14 · 코드 15 · 코드 16 · 코드 17 · 코드 18 · 코드 19
Multigraph ChebNet	Accuracy 89.1%	Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules 2018-11-23	코드 1
InfoGraph	Accuracy 89.01%	InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization 2019-07-31	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5