Graph Classification On Cifar10 100K

Accuracy (%)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy (%)	Paper Title	Repository
GAT	65.48	Graph Attention Networks	-
GRIT	76.468	Graph Inductive Biases in Transformers without Message Passing	-
GRED	76.853±0.185	Recurrent Distance Filtering for Graph Representation Learning	-
PNA	70.47	Principal Neighbourhood Aggregation for Graph Nets	-
Exphormer	74.754±0.194	Exphormer: Sparse Transformers for Graphs	-
ESA (Edge set attention, no positional encodings)	75.413±0.248	An end-to-end attention-based approach for learning on graphs	-
GraphSage	66.08	Inductive Representation Learning on Large Graphs	-
EGT	68.702	Global Self-Attention as a Replacement for Graph Convolution	-
MoNet	53.42	Geometric deep learning on graphs and manifolds using mixture model CNNs	-
GatedGCN	67.312	Benchmarking Graph Neural Networks	-
GraphGPS + HDSE	76.180±0.277	Enhancing Graph Transformers with Hierarchical Distance Structural Encoding	-
EIGENFORMER	70.194	Graph Transformers without Positional Encodings	-
ARGNP	73.90	Automatic Relation-aware Graph Network Proliferation	-
GIN	53.28	How Powerful are Graph Neural Networks?	-
GatedGCN+	77.218 ± 0.381	Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence	-
TIGT	73.955	Topology-Informed Graph Transformer	-
NeuralWalker	80.027 ± 0.185	Learning Long Range Dependencies on Graphs via Random Walks	-
GatedGCN	69.37	Residual Gated Graph ConvNets	-
GPS	72.298	Recipe for a General, Powerful, Scalable Graph Transformer	-
DGN	72.84	Directional Graph Networks	-

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