Node Classification on Brazil 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)	0.516	No	Semi-Supervised Classification with Graph Convol...	2016-09-09	Code
2	Union (Li et al., 2018)	0.466	No	Deeper Insights into Graph Convolutional Network...	2018-01-22	Code
3	Intersection (Li et al., 2018)	0.459	No	Deeper Insights into Graph Convolutional Network...	2018-01-22	Code
4	GraphSAGE (Hamilton et al., [2017a])	0.404	No	Inductive Representation Learning on Large Graphs	2017-06-07	Code
5	GAT (Velickovic et al., 2018)	0.382	No	Graph Attention Networks	2017-10-30	Code

#1GCN_cheby (Kipf and Welling, 2017)SOTA
0.516
Accuracy· 2016-09-09
Semi-Supervised Classification with Graph Convolutional Networks Code
#2Union (Li et al., 2018)
0.466
Accuracy· 2018-01-22
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning Code
#3Intersection (Li et al., 2018)
0.459
Accuracy· 2018-01-22
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning Code
#4GraphSAGE (Hamilton et al., [2017a])
0.404
Accuracy· 2017-06-07
Inductive Representation Learning on Large Graphs Code
#5GAT (Velickovic et al., 2018)
0.382
Accuracy· 2017-10-30
Graph Attention Networks Code