Node Classification on Wiki-Vote

Metric: Accuracy (higher is better)

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	DEMO-Net(weight)	99.8	No	DEMO-Net: Degree-specific Graph Neural Networks ...	2019-06-05	Code
2	GAT (Velickovic et al., 2018)	59.4	No	Graph Attention Networks	2017-10-30	Code
3	GCN_cheby (Kipf and Welling, 2017)	49.5	No	Semi-Supervised Classification with Graph Convol...	2016-09-09	Code
4	Union (Li et al., 2018)	46.3	No	Deeper Insights into Graph Convolutional Network...	2018-01-22	Code
5	GCN (Kipf and Welling, 2017)	32.9	No	Semi-Supervised Classification with Graph Convol...	2016-09-09	Code
6	GraphSAGE (Hamilton et al., [2017a])	24.5	No	Inductive Representation Learning on Large Graphs	2017-06-07	Code

#1DEMO-Net(weight)SOTA
99.8
Accuracy· 2019-06-05
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification Code
#2GAT (Velickovic et al., 2018)SOTA
59.4
Accuracy· 2017-10-30
Graph Attention Networks Code
#3GCN_cheby (Kipf and Welling, 2017)SOTA
49.5
Accuracy· 2016-09-09
Semi-Supervised Classification with Graph Convolutional Networks Code
#4Union (Li et al., 2018)
46.3
Accuracy· 2018-01-22
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning Code
#5GCN (Kipf and Welling, 2017)
32.9
Accuracy· 2016-09-09
Semi-Supervised Classification with Graph Convolutional Networks Code
#6GraphSAGE (Hamilton et al., [2017a])
24.5
Accuracy· 2017-06-07
Inductive Representation Learning on Large Graphs Code