Scientific Reports

Table 3 Performance of node classification in micro-F1 and macro-F1.

From: Leveraging graph-based hierarchical medical entity embedding for healthcare applications

Algorithms	Micro-F1				Macro-F1
Algorithms	20%	40%	60%	80%	20%	40%	60%	80%
ME2Vec	0.869	0.877	0.878	0.879	0.664	0.679	0.682	0.676
metapath2vec	0.865	0.868	0.869	0.870	0.522	0.551	0.574	0.577
node2vec (service)	0.865	0.875	0.876	0.878	0.613	0.630	0.632	0.640
node2vec (doctor)	0.850	0.862	0.860	0.861	0.474	0.466	0.462	0.463
LINE (service)	0.855	0.864	0.866	0.866	0.587	0.592	0.592	0.586
LINE (doctor)	0.854	0.863	0.860	0.861	0.470	0.465	0.462	0.463
SC (service)	0.862	0.861	0.861	0.868	0.463	0.463	0.463	0.465
SC (doctor)	0.862	0.861	0.861	0.868	0.463	0.463	0.463	0.465
NMF (service)	0.868	0.870	0.869	0.879	0.584	0.586	0.589	0.600
NMF (doctor)	0.861	0.860	0.860	0.867	0.469	0.472	0.470	0.469

Bolded numbers indicate best performance compared with the rest in the column.

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