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Finally, we compare FVCC with Girvan-Newman algorithm (GN), LPA, the algorithm using eigenvectors of matrices (EV), spin glass model (SG), LPAm, and LPAp on three real-world networks whose community structures are known in Table 6 and list the mean modularity Q and number of communities Nc by these algorithms on six real-world networks for comparison in Table 7 [2, 4, 5, 9, 10, 15, 16].
As shown in Tables 6 and 7, though the quality of network division by LPAp is a little shy of the modularity for SG, it is still higher than the modularity by most other algorithms, such as CNM, EV, and LPAm. Moreover, FVCC by LPAp is as high as SG.
Algorithm Club Football Dolphin GN 97.06 83.48 98.39 CNM 97.06 63.48 96.77 LPA 96.47 61.75 83.23 EV 97.06 86.26 98.39 SG 97.06 83.48 98.39 LPAm 97.06 83.48 96.77 LPAp 97.06 83.48 98.39 TABLE 7: Comparison between the results of network division by different algorithms on six real-world networks.
First, we analyze the results of the algorithms NILP and LPAm in Zachary's Karate network, as shown in Figure 5.
Figure 6 illustrates the comparison of accuracy for community detection of four label propagation based algorithms LPA, LPAm, LHLC, and 2-NILP.
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