HyperAI超神経

ホームニュース最新論文チュートリアルデータセット百科事典 SOTA LLMモデル GPU ランキング学会

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HyperAI超神経

Graph Classification On Mnist

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

モデル名	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	-

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Graph Classification On Mnist | SOTA | HyperAI超神経