Node Classification On Texas 60 20 20 Random

المقاييس

1:1 Accuracy

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	1:1 Accuracy	Paper Title	Repository
MLP-2	92.26 ± 0.71	Revisiting Heterophily For Graph Neural Networks	-
GCNII*	88.52 ± 3.02	Simple and Deep Graph Convolutional Networks	-
Geom-GCN*	67.57	Geom-GCN: Geometric Graph Convolutional Networks	-
ACMII-GCN	95.08 ± 2.07	Revisiting Heterophily For Graph Neural Networks	-
ACM-SGC-2	93.44 ± 2.54	Revisiting Heterophily For Graph Neural Networks	-
APPNP	91.18 ± 0.70	Predict then Propagate: Graph Neural Networks meet Personalized PageRank	-
BernNet	93.12 ± 0.65	BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation	-
ACM-GCNII	92.46 ± 1.97	Revisiting Heterophily For Graph Neural Networks	-
ACM-GCN++	96.56 ± 2	Revisiting Heterophily For Graph Neural Networks	-
NFGNN	94.03±0.82	Node-oriented Spectral Filtering for Graph Neural Networks	-
GCN+JK	80.66 ± 1.91	Revisiting Heterophily For Graph Neural Networks	-
SGC-2	81.31 ± 3.3	Simplifying Graph Convolutional Networks	-
GraphSAGE	79.03 ± 1.20	Inductive Representation Learning on Large Graphs	-
GCNII	82.46 ± 4.58	Simple and Deep Graph Convolutional Networks	-
ACM-GCN+	94.92 ± 2.79	Revisiting Heterophily For Graph Neural Networks	-
GPRGNN	92.92 ± 0.61	Adaptive Universal Generalized PageRank Graph Neural Network	-
ACM-SGC-1	93.61 ± 1.55	Revisiting Heterophily For Graph Neural Networks	-
ACMII-Snowball-2	95.25 ± 1.55	Revisiting Heterophily For Graph Neural Networks	-
HH-GAT	80.54 ± 4.80	Half-Hop: A graph upsampling approach for slowing down message passing	-
GAT+JK	75.41 ± 7.18	Revisiting Heterophily For Graph Neural Networks	-

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