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홈
SOTA
Graph Classification
Graph Classification On Mutag
Graph Classification On Mutag
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
Paper Title
Repository
GAP-Layer (Ncut)
86.9%
DiffWire: Inductive Graph Rewiring via the Lovász Bound
VRGC
86.3%
Variational Recurrent Neural Networks for Graph Classification
GDL-g (SP)
87.09%
Online Graph Dictionary Learning
GIN-0
89.4%
How Powerful are Graph Neural Networks?
WKPI-kcenters
87.5%
Learning metrics for persistence-based summaries and applications for graph classification
FGW wl h=2 sp
86.42%
Optimal Transport for structured data with application on graphs
ApproxRepSet
86.33%
Rep the Set: Neural Networks for Learning Set Representations
Function Space Pooling
83.3%
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
NDP
84.7%
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
PATCHY-SAN
92.63%
Learning Convolutional Neural Networks for Graphs
Path up to length h
88.47%
Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons
SPI-GCN
84.40%
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network
-
DGCNN
85.83%
An End-to-End Deep Learning Architecture for Graph Classification
P-WL-C
-
A Persistent Weisfeiler–Lehman Procedure for Graph Classification
-
edGNN (max)
88.8%
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
hGANet
90.00%
Graph Representation Learning via Hard and Channel-Wise Attention Networks
CT-Layer
87.58%
DiffWire: Inductive Graph Rewiring via the Lovász Bound
U2GNN (Unsupervised)
88.47%
Universal Graph Transformer Self-Attention Networks
R-GIN + PANDA
88.2%
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
k-GNN
86.1%
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
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