Accueil Actualités Articles de recherche récents Tutoriels Ensembles de données Wiki SOTA Modèles LLM Classement GPU Événements

Français

Graph Classification On Mnist

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
GCN+	98.382 ± 0.095	Can Classic GNNs Be Strong Baselines 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	Can Classic GNNs Be Strong Baselines 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	-

0 of 13 row(s) selected.

Graph Classification On Mnist | SOTA | HyperAI