Node Classification on Europe Air-Traffic

Metric: Accuracy (higher is better)

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	GCN_cheby (Kipf and Welling, 2017)	46	No	Semi-Supervised Classification with Graph Convol...	2016-09-09	Code
2	DEMO-Net(weight)	45.9	No	DEMO-Net: Degree-specific Graph Neural Networks ...	2019-06-05	Code
3	Intersection (Li et al., 2018)	44.3	No	Deeper Insights into Graph Convolutional Network...	2018-01-22	Code
4	GAT (Velickovic et al., 2018)	42.4	No	Graph Attention Networks	2017-10-30	Code
5	GCN (Kipf and Welling, 2017)	37.1	No	Semi-Supervised Classification with Graph Convol...	2016-09-09	Code
6	GraphSAGE (Hamilton et al., [2017a])	27.2	No	Inductive Representation Learning on Large Graphs	2017-06-07	Code

#1GCN_cheby (Kipf and Welling, 2017)SOTA
46
Accuracy· 2016-09-09
Semi-Supervised Classification with Graph Convolutional Networks Code
#2DEMO-Net(weight)
45.9
Accuracy· 2019-06-05
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification Code
#3Intersection (Li et al., 2018)
44.3
Accuracy· 2018-01-22
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning Code
#4GAT (Velickovic et al., 2018)
42.4
Accuracy· 2017-10-30
Graph Attention Networks Code
#5GCN (Kipf and Welling, 2017)
37.1
Accuracy· 2016-09-09
Semi-Supervised Classification with Graph Convolutional Networks Code
#6GraphSAGE (Hamilton et al., [2017a])
27.2
Accuracy· 2017-06-07
Inductive Representation Learning on Large Graphs Code