An end-to-end graph convolutional kernel support vector machine

Applied Network Science

Table 2 For each dataset, the mean classification accuracy plus standard deviation of 10-fold cross validation for each graph classification model are displayed

Model	MUTAG	PTC_MR	BZR_MD	PTC_FM	COX2
Graphlet	0.86 ±0.05	0.54 ±0.08	0.61 ±0.12	0.57 ±0.07	0.75 ±0.06
Shortest path	0.82 ±0.06	0.55 ±0.08	0.68 ±0.07	0.55 ±0.07	0.77 ±0.06
Vertex histogram	0.85 ±0.05	0.58 ±0.07	0.70 ±0.08	0.58 ±0.06	0.75 ±0.06
Weisfeiler Lehman	0.71 ±0.06	0.59 ±0.07	0.60 ±0.07	0.63 ±0.08	0.65 ±0.08
Pyramid match	0.86 ±0.06	0.54 ±0.06	0.62 ±0.07	0.57 ±0.09	0.73 ±0.06
GCN	0.73 ±0.06	0.57 ±0.03	0.68 ±0.06	0.61 ±0.08	0.79 ±0.03
GCNWithJK	0.73 ±0.07	0.57 ±0.05	0.68 ±0.07	0.62 ±0.08	0.78 ±0.02
GIN	0.82 ±0.07	0.54 ±0.05	0.62 ±0.09	0.57 ±0.07	0.80 ±0.04
GIN0	0.85 ±0.04	0.57 ±0.08	0.63 ±0.13	0.59 ±0.05	0.77 ±0.04
GINWithJK	0.83 ±0.07	0.55 ±0.07	0.61 ±0.15	0.60 ±0.04	0.80 ±0.06
GIN0WithJK	0.83 ±0.06	0.54 ±0.07	0.63 ±0.10	0.59 ±0.06	0.82 ±0.05
GraphSAGE	0.72 ±0.06	0.57 ±0.08	0.68 ±0.09	0.61 ±0.06	0.78 ±0.01
GraphSAGEWithJK	0.71 ±0.09	0.56 ±0.04	0.68 ±0.09	0.60 ±0.07	0.77 ±0.01
DiffPool	0.84 ±0.12	0.57 ±0.03	0.69 ±0.07	0.61 ±0.06	0.77 ±0.02
GlobalAttentionNet	0.74 ±0.07	0.56 ±0.05	0.67 ±0.06	0.63 ±0.06	0.77 ±0.01
Set2SetNet	0.73 ±0.07	0.56 ±0.03	0.68 ±0.10	0.62 ±0.06	0.78 ±0.04
SortPool	0.75 ±0.11	0.59 ±0.08	0.66 ±0.09	0.60 ±0.08	0.77 ±0.01
Proposed model	0.87 ±0.06	0.60 ±0.08	0.62 ±0.10	0.62 ±0.06	0.79 ±0.05