[Retracted] Research on Optimization of Food Industry Processing Process Based on Computational Intelligence

Table 3

Split prediction of the processing flow.

Type	Model	Lazega	CKM	C.elegans	H.genetic	PPI	Twitter
Single-layered	DecpWalk (avg)	0.796	0.764	0.703	0.618	0.689	0.586
	DecpWalk (onca)	0.802	0.773	0.716	0.623	0.696	0.593
	LINE (avg)	0.703	0.634	0.621	0.558	0.664	0.516
	LINE (coneat)	0.692	0.626	0.625	0.574	0.670	0.527
	nodc2vec (avg)	0.798	0.781	0.725	0.647	0.726	0.581
	node2vec (concat)	0.805	0.787	0.742	0.652	0.730	0.597
	mctapath2 vec	0.806	0.775	0.722	0.625	0.692	0.595
Multilayer	OhmNct (avg)	0.762	0.740	0.773	0.712	0.695	0.556
	OhmNct (concat)	0.764	0.743	0.785	0.716	0.704	0.563
	MNE (avg)	0.828	0.867	0.821	0.770	0.697	0.604
	MNE (concat)	0.833	0.871	0.825	0.778	0.702	0.617
	MVE	0.815	0.912	0.828	0.826	0.722	0.645
	HMNE	0.822	0.923	0.835	0.843	0.747	0.653