HyperAI초신경

홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

한국어

HyperAI초신경

Graph Classification On Mnist

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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 | HyperAI초신경