# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 4 Experiment
### Comparison Results between MCEP and Baselines  
This section shows the effects of MCEP and other algorithms on three datasets. The experimental results are shown in Figures 3-8. The last column in each figure is the average value of each algorithm on the corresponding dataset. On the six-askubuntu-c2q dataset, because the number of negative samples of survival events is too small, the prediction accuracy of the events cannot be compared. The experimental results show that the MCEP achieves the best results in most of the evolutionary events of the three datasets, which shows the effectiveness of the MCEP. For some evolutionary events, such as the survival event of ia-reality-call, the effect of MCEP algorithm is slightly worse than other algorithms. This may be because different community evolution events often present different features, and these features will also be different on the different datasets. Therefore, it is difficult to find a common feature set that enables the MCEP to have the best effect on all evolutionary events.  
Figure 3: Comparing the accuracy of the algorithms on the DBLP dataset.  
As can be seen from Figures 3-8, the effect of the Cnode2 vec and CLINE algorithms is slightly lower than the effect of the MCEP algorithm on the three datasets. But the effect of the Cnode2 vec and CLINE algorithm is better than other algorithms. The common point of MCEP, Cnode2 vec, and CLINE algorithms is that these three algorithms use a relatively complex method to construct feature sets. MCEP is based on community roles and mesoscopic features, and Cnode2 vec and CLINE are based on graph representation learning. In addition, the dimensionality of the features obtained by these three methods is also more than other algorithms, so it is more able to capture the characteristics of the features.  
Figure 4: Comparing the f1 score of the algorithms on the DBLP dataset.  
Figure 5: Comparing the accuracy of the algorithms on ia-reality-call dataset.  
community evolution. This shows that due to the complex characteristics of the community evolution, it is difficult to achieve a satisfactory effect for community evolution prediction only by selecting a few suitable features.  
Figure 6: Comparing the f1 score of the algorithms on ia-reality-call dataset.  
Figure 7: Comparing the accuracy of the algorithms on sx-askubuntu-c2q dataset.  
The experiment result shows that the GL-B-MCEP algorithm can achieve the best results on the three datasets. Due to the complex characteristics of community evolution events, GL-B-MCEP did not achieve the best results in all community evolution events, which is the same as the results of the comparative experiment. But from an average point of view, the effect of GL-B-MCEP is the best.  
In addition, on the DBLP dataset, the introduction of community mesoscopic features has greatly improved the prediction effect of the algorithm, while the improvement of the GLRE algorithm is not obvious. This may be because the DBLP dataset has only 2423 nodes. On this small-scale network, the local features of the community are not obvious. On the other two datasets, it can be seen that with the increase of network scale, the improvement of the prediction effect of the GLRE algorithm is gradually obvious, which shows that GLRE can be more effective on large-scale networks.