# Dynamic Community Detection Based on Evolutionary DeepWalk
## 4 Experimental Analysis
### Network Evaluation
#### 4.2.2 Overall Network Analysis  
This section mainly discusses the community detection performance of each algorithm to dynamic networks. Figure 4 shows the effect of the value change of \(\mu\) in the dynamic artificial LFR networks on the average evaluation results of each algorithm.  
It can be seen from Figure 4 that, as the value of the complexity coefficient \(\mu\) increases, the average evaluation results of all algorithms for the artificial network show a decreasing trend. This is because, when \(\mu\) increases, the connection between communities in the LFR networks also increases, which results in the edge tightness of the inter-community and inner-community are not very different. Thus, it is more difficult to divide the community structure.  
Figure 3: Avg evaluation results of different \(\alpha\) values in the real-world datasets. (**a**) Avg evaluation of different \(\alpha\) with CW. (**b**) Avg evaluation of different \(\alpha\) with PS.  
Figure 4 can be divided into two areas:  
1. When \(\mu\leq 0.5\), DeepWalk achieves the best results. It has more outstanding processing performance in dealing with relatively simple networks than other dynamic graph embedding algorithms and community detection algorithms.
2. When \(\mu\geq 0.6\), DEDW has the best performance. It can be inferred that our method has a good dynamic community partition effect for the complex networks.  
In addition, the average evaluation results of Louvain in the LFR networks are poor. The reason for these results may be that the generated artificial network community structure is not obvious, and the modularity is hard to be measured. When \(\mu\) is between  
Figure 4: Avg evaluation results of each algorithm in LFR datasets. (**a**) Avg FMI of algorithms with LFR. (**b**) Avg purity of algorithms with LFR. (**c**) Avg NMI of algorithms with LFR. (**d**) Avg homo of algorithms with LFR. (**e**) Avg comp of algorithms with LFR. (**f**) Avg V-measure of algorithms with LFR.  
[0.3, 0.6], the evaluation results of DynGEM and DynAE have a large decline. It may be because these algorithms have better embedding performance for simpler networks, but for complex network structures, the ability of the algorithm to extract node features needs to be improved.  
With the analysis of the above results, our method has good detection performance for more complex social networks. For networks with simple social relationships, we need to improve the detection performance of DEDW. Figure 5 describes the average evaluation results of each algorithm in real-world dynamic networks.  
As can be seen from Figure 5, DEDW has the best evaluation results on the community detection of the four real-world network datasets. The performance of the DeepWalk is lower than DEDW. The reason is that DEDW considers the entire network evolution process, uses the influence of historical network structure on the current network and uses graph embedding methods to obtain node features of the network, to obtain more accurate community results. The purity and homo evaluation results of Dyperm in HS2011, HS2012, and PS are higher than other algorithms. It is found that the evaluation meaning of purity and homo is the same, both focus on finding and processing the nodes belonging to the same real community in the divided community, which indicates that the community results divided by Dyperm focus more on homogeneity. The Louvain algorithm found relatively good results for real-world network communities, indicating that real-world networks are more in line with the community characteristics of high cohesion and low coupling.  
Figure 5: Avg evaluation results of each algorithm in the real-world dataset. (**a**) Avg results of algorithms with HS2011. (**b**) Avg results of algorithms with HS2012. (**c**) Avg results of algorithms with PS. (**d**) Avg results of algorithms with CW.