Node Classification On Film 60 20 20 Random

المقاييس

1:1 Accuracy

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	1:1 Accuracy	Paper Title
Geom-GCN*	31.63	Geom-GCN: Geometric Graph Convolutional Networks
OptBasisGNN	42.39 ± 0.52	Graph Neural Networks with Learnable and Optimal Polynomial Bases
FavardGNN	43.05 ± 0.53	Graph Neural Networks with Learnable and Optimal Polynomial Bases
MLP-2	38.58 ± 0.25	Revisiting Heterophily For Graph Neural Networks
NHGCN	43.94 ± 1.14	Neighborhood Homophily-Guided Graph Convolutional Network
ACM-GCN+	41.79 ± 1.01	Revisiting Heterophily For Graph Neural Networks
GCNII	40.82 ± 1.79	Simple and Deep Graph Convolutional Networks
APPNP	38.86 ± 0.24	Predict then Propagate: Graph Neural Networks meet Personalized PageRank
BernNet	41.79 ± 1.01	BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
FAGCN	31.59 ± 1.37	Beyond Low-frequency Information in Graph Convolutional Networks
ACM-SGC-1	39.33 ± 1.25	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+	41.5 ± 1.54	Revisiting Heterophily For Graph Neural Networks
GCN+JK	32.72 ± 2.62	Revisiting Heterophily For Graph Neural Networks
ACM-GCNII	41.37 ± 1.37	Revisiting Heterophily For Graph Neural Networks
UGT	36.84±0.62	Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
SGC-2	28.81 ± 1.11	Simplifying Graph Convolutional Networks
ACMII-Snowball-3	40.31 ± 1.6	Revisiting Heterophily For Graph Neural Networks
Snowball-3	36.00 ± 1.36	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GCN	35.51 ± 0.99	Semi-Supervised Classification with Graph Convolutional Networks
ACM-Snowball-2	41.4 ± 1.23	Revisiting Heterophily For Graph Neural Networks

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