From Table 7, we observe that the SGGM has the lowest error rate among all methods shown.
For SGGM method, A is set to 0-01; 90% of data was used as training set.
In order to prove the performance of SGGM, we compare the proposed method with the other thirteen methods on these large datasets (for details see Table 8) in terms of AUC and error rate (data sources: (1) http://konect.uni-koblenz .de/networks/ (Email).
As observed from Table 9, AUC of SGGM is highest on all 4 datasets.
In the comparative analysis of the performance of the 14 methods using the eight real-world datasets, the SGGM method was outstanding in terms of AUC and error rate.
We sampled the original network adjacency matrix and used the SGGM method to depict the network structure.
Most nodes are independent in the actual network; thus, we used the SGGM method to estimate a precision matrix of the adjacency matrix to predict links.
Caption: FIGURE 2: AUC of the SGGM (four datasets, training set ratio = 90%).
Caption: FIGURE 3: AUC of the SGGM (four datasets, sample scale = 0.5N).
Caption: FIGURE 4: Comparison of ROC metric of four methods (SGGM, CN, AA, and PA) on four datasets.