Node Classification On Squirrel 60 20 20

المقاييس

1:1 Accuracy

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	1:1 Accuracy	Paper Title	Repository
GCN	44.76 ± 1.39	Semi-Supervised Classification with Graph Convolutional Networks
ACM-SGC-2	40.91 ± 1.39	Revisiting Heterophily For Graph Neural Networks
GraphSAGE	41.26 ± 0.26	Inductive Representation Learning on Large Graphs
ACM-SGC-1	46.4 ± 1.13	Revisiting Heterophily For Graph Neural Networks
GAT	42.72 ± 0.33	Graph Attention Networks
ACM-Snowball-3	55.73 ± 2.39	Revisiting Heterophily For Graph Neural Networks
NFGNN	58.9±0.35	Node-oriented Spectral Filtering for Graph Neural Networks
ACM-Snowball-2	55.97 ± 2.03	Revisiting Heterophily For Graph Neural Networks
HH-GCN	47.19 ± 1.21	Half-Hop: A graph upsampling approach for slowing down message passing
GPRGNN	49.93 ± 0.53	Adaptive Universal Generalized PageRank Graph Neural Network
SGC-2	41.25 ± 1.4	Simplifying Graph Convolutional Networks
ACMII-GCN++	69.98 ± 1.53	Revisiting Heterophily For Graph Neural Networks
Snowball-2	47.88 ± 1.23	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GNNDLD	77.72±0.84	GNNDLD: Graph Neural Network with Directional Label Distribution	-
GCN+JK	53.40 ± 1.90	Revisiting Heterophily For Graph Neural Networks
APPNP	34.77 ± 0.34	Predict then Propagate: Graph Neural Networks meet Personalized PageRank
GAT+JK	52.28 ± 3.61	Revisiting Heterophily For Graph Neural Networks
BernNet	51.35 ± 0.73	BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
MLP-2	31.28 ± 0.27	Revisiting Heterophily For Graph Neural Networks
HH-GraphSAGE	45.25 ± 1.52	Half-Hop: A graph upsampling approach for slowing down message passing

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