# Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks
## 4 Results
### Real-World Networks  
#### 4.2.1 MIT Reality Mining  
This experiment is conducted on the MIT dataset [52]. The dataset is collected by recording the mobile phone activity of 94 students and employees over a year. The dataset built a dynamic network based on physical distance, which is measured by scanning nearby Bluetooth devices every 5 min. Data collected near the beginning and end of experiments with low participation rates are excluded in this experiment. Each time step corresponds to one week, so there are 37 time steps between August 2004 and May 2005. Figure 6 shows the mean-variance error results of the proposed novel method and four representative models under the artificial network.  
Figure 6 shows that, under the MSE evaluation index, the smaller the error, the better the result; that is, the closer the model image is to the \(x\)-axis. Obviously, compared with other colors (the other four models), the image with blue color (the proposed method in this paper) is closer to the \(x\)-axis; that is, the proposed PPPM method has a lower MSE value and a smaller error. Table 3 compares the average ARI results of the proposed method with those of the four representative models in the real network (MIT reality mining) environment.  
In Table 3, the bold font shows that the result is the best, you can clearly see that the proposed PPPM in all parameters (the maximum value; the first 75% of the value; the median; the first 25% of the value; minimum value) cases are the best and clear, the average performance of PPPM performance (median) than the best model is increased by 3% in the other four. Figure 7 shows the comparison of MARI values on the MIT dataset between the proposed method and four state-of-the-art models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **FaceNet** & **PPPM** & **SBM + MLE** & **CLBM** & **PisCES** \\ \hline Max & 0.8991 & **0.9555** & 0.8678 & 0.8876 & 0.9412 \\
75\% & 0.7125 & **0.8005** & 0.7856 & 0.6258 & 0.7811 \\ Median & 0.4902 & **0.6523** & 0.6215 & 0.4981 & 0.6536 \\
25\% & 0.3154 & **0.5111** & 0.4992 & 0.2314 & 0.4902 \\ Min & 0.1002 & **0.40** & 0.2671 & 0.1487 & 0.3243 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the proposed PPPM method and representative model on the Mean ARI (real data).  
Figure 6: Comparison of the proposed method with 4 different models on MSE (synthetic network).  
Figure 7: Comparison of the proposed model with 4 different models on MARI (reality network).  
In Figure 7, the upper and lower edges of each box in the boxplot represent 25% and 75% values, respectively, and the middle red line denotes the median. It is obvious that PPPM, SEM + MLE, and the three boxes perform better than the other two model boxes. Among the three models with better performance, the PPPM box position is slightly higher than that of SEM + MLE and PisCES boxes, and the median value is also slightly higher than that of SEM + MLE and PisCES models. In conclusion, compared with the other four representative models in the real network, the proposed dynamic community PPPM method performs better under the two evaluation indexes of prediction accuracy and error.