HyperAI초신경

Graph Classification On Mutag

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Accuracy
Paper TitleRepository
GAP-Layer (Ncut)86.9%DiffWire: Inductive Graph Rewiring via the Lovász Bound
VRGC86.3%Variational Recurrent Neural Networks for Graph Classification
GDL-g (SP)87.09%Online Graph Dictionary Learning
GIN-089.4%How Powerful are Graph Neural Networks?
WKPI-kcenters87.5%Learning metrics for persistence-based summaries and applications for graph classification
FGW wl h=2 sp86.42%Optimal Transport for structured data with application on graphs
ApproxRepSet86.33%Rep the Set: Neural Networks for Learning Set Representations
Function Space Pooling83.3%IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
NDP84.7%Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
PATCHY-SAN92.63%Learning Convolutional Neural Networks for Graphs
Path up to length h88.47%Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons
SPI-GCN84.40%SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network-
DGCNN85.83%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
hGANet90.00%Graph Representation Learning via Hard and Channel-Wise Attention Networks
CT-Layer87.58%DiffWire: Inductive Graph Rewiring via the Lovász Bound
U2GNN (Unsupervised)88.47%Universal Graph Transformer Self-Attention Networks
R-GIN + PANDA88.2%PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
k-GNN86.1%Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
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