## 4 Results Evaluation
### Model Validation
#### 4.2.2 Empirical Macro Validation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**2G**} & \multicolumn{2}{c}{**3G**} & \multicolumn{2}{c}{**4G**} \\ \cline{2-7}
**Blocks (Multiple** & \multicolumn{1}{c}{**PAN**} & \multicolumn{1}{c}{**WSN**} & \multicolumn{1}{c}{**PAN**} & \multicolumn{1}{c}{**WSN**} & \multicolumn{1}{c}{**PAN**} & \multicolumn{1}{c}{**WSN**} \\ \hline Block 1 (1–100) & 0.995914888 & 0.995622317 & 0.986461356 & 0.985801933 & 0.996891628 & 0.996891628 \\ Block 2 (101–200) & 0.995956402 & 0.996166451 & 0.982544504 & 0.985436519 & 0.996891628 & 0.996891628 \\ Block 3 (201–300) & 0.994838045 & 0.995984549 & 0.972365054 & 0.986351149 & 0.996891628 & 0.996891628 \\ Block 4 (301–400) & 0.995916081 & 0.99687907 & 0.985623227 & 0.9858684 & 0.996891628 & 0.996891628 \\ Block 5 (401–500) & 0.995132448 & 0.995636979 & 0.979471748 & 0.985703357 & 0.996891628 & 0.996891628 \\ Block 6 (501–600) & 0.994698311 & 0.995902483 & 0.985212857 & 0.985595798 & 0.996891628 & 0.996891628 \\ Block 7 (601–700) & 0.995230473 & 0.995425341 & 0.986106428 & 0.986400827 & 0.996891628 & 0.996891628 \\ Block 8 (701–800) & 0.99433489 & 0.994783757 & 0.982773917 & 0.985401794 & 0.996849922 & 0.996961433 \\ Block 9 (801–900) & 0.995719691 & 0.995470086 & 0.985447319 & 0.984708019 & 0.996891628 & 0.996891628 \\ Block 10 & 0.995928753 & 0.995132448 & 0.98628003 & 0.986012813 & 0.996891628 & 0.996891628 \\ Block 11 & 0.995329409 & 0.994755081 & 0.981240056 & 0.986103608 & 0.996905604 & 0.996891628 \\ Block 12 & 0.99492104 & 0.996601752 & 0.982942205 & 0.985013477 & 0.996891628 & 0.996891628 \\ Block 13 & & & & & & \\ (1201–1300) & 0.996459663 & 0.996043596 & 0.977795559 & 0.984477854 & 0.996849922 & 0.996891628 \\ Block 14 & & & & & & \\ (1301–1400) & 0.99482494 & 0.994937795 & 0.985328098 & 0.984968097 & 0.996891628 & 0.996891628 \\ Block 15 & & & & & & \\ (1401–1500) & 0.995564687 & 0.994684451 & 0.986315611 & 0.98671933 & 0.996891628 & 0.996891628 \\ Total Mean & 0.995385 & 0.995601786 & 0.983060531 & 0.985595532 & 0.996886999 & 0.996896282 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Spearman correlation coefficient Test results in 1–100 runs (block 1). \begin{table}
\begin{tabular}{c} average simulated data \\ of 4G generated in WSN \\ \end{tabular} & 111.87 & \textless{}2.2e–16 & 0.9968916 & Rejected \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Social networks performance in simulating the diffusion of mobile telecommunication technologies (2G, 3G, and 4G). In terms of 3G simulation, \(\mathrm{P_{WSN}}\) is again better than \(\mathrm{P_{PAN}}\), as seen in Figure 12. All of these comparisons are presented in column 3 of Table 13.  
As can be seen in Figure 13 and column 4 of Table 13, the performance of WSN and PAN is a bit different in terms of 4G simulation, but WSN shows a better performance again. All results show that WSN has had a better performance in replicating the diffusion of MTTs in Iran; therefore, WSN seems to be the best explanation for the social structure of Iranian Society. A large number of studies support this finding that small-world networks are an efficient explanation for social networks [67; 80; 96; 97; 98] where the clustering coefficient (CC) is high (i.e., 0.34 for this study because of the size of sample, but it tends to \(\frac{3}{4}\) (0.75) in case of large N) and average path length (L) is low (i.e., 2.90 for this study, but it will approximately converge to 7.90 for real number of Iranian population). High CC indicates that the friends of a person tend to be friends of each other, and low L shows that when two persons want to reach each other, they can do it by a limited number of steps. This network structure is highly efficient in information transfer where the world-of-mouth plays a critical role [100; 101]. Figure 11: Performance of social structures in the simulation of 2G. Figure 12: Performance of social structures in the simulation of 3G.