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SOTA
Graph Classification
Graph Classification On Proteins
Graph Classification On Proteins
評価指標
Accuracy
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Accuracy
Paper Title
Repository
R-GCN + PANDA
76
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
GIN
75.536±1.851
How Powerful are Graph Neural Networks?
Multigraph ChebNet
76.5%
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
ApproxRepSet
70.74%
Rep the Set: Neural Networks for Learning Set Representations
EigenGCN-3
76.60%
Graph Convolutional Networks with EigenPooling
Shortest-Path Kernel
76.4%
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
WEGL
76.5%
Wasserstein Embedding for Graph Learning
GIC
77.65%
Gaussian-Induced Convolution for Graphs
-
2-WL-GNN
76.5
A Novel Higher-order Weisfeiler-Lehman Graph Convolution
1-NMFPool
72.1%
A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks
-
SPI-GCN
72.06%
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network
-
GraphGPS
77.143±1.494
Recipe for a General, Powerful, Scalable Graph Transformer
FGW sp
74.55%
Optimal Transport for structured data with application on graphs
GIN-0
76,2%
How Powerful are Graph Neural Networks?
GCAPS-CNN
76.40%
Graph Capsule Convolutional Neural Networks
DiffWire
74.91%
DiffWire: Inductive Graph Rewiring via the Lovász Bound
GANet
77.92%
Graph Representation Learning via Hard and Channel-Wise Attention Networks
GDL-g (SP)
74.86
Online Graph Dictionary Learning
GNN (DiffPool)
76.25%
Hierarchical Graph Representation Learning with Differentiable Pooling
PPGN
77.20%
Provably Powerful Graph Networks
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