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SOTA
Graph Classification
Graph Classification On Mnist
Graph Classification On Mnist
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Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
Paper Title
Repository
GCN+
98.382 ± 0.095
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
EIGENFORMER
98.362
Graph Transformers without Positional Encodings
-
NeuralWalker
98.760 ± 0.079
Learning Long Range Dependencies on Graphs via Random Walks
GatedGCN
97.340
Benchmarking Graph Neural Networks
EGT
98.173
Global Self-Attention as a Replacement for Graph Convolution
Exphormer
98.414±0.038
Exphormer: Sparse Transformers for Graphs
ESA (Edge set attention, no positional encodings)
98.753±0.041
An end-to-end attention-based approach for learning on graphs
-
GPS
98.05
Recipe for a General, Powerful, Scalable Graph Transformer
CKGCN
98.423
CKGConv: General Graph Convolution with Continuous Kernels
TIGT
98.230±0.133
Topology-Informed Graph Transformer
GatedGCN+
98.712 ± 0.137
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
GRIT
98.108
Graph Inductive Biases in Transformers without Message Passing
ESA (Edge set attention, no positional encodings, tuned)
98.917±0.020
An end-to-end attention-based approach for learning on graphs
-
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