# An Information-Theoretic Approach for Detecting Community Structure Based on Network Representation
## 4 Experiments
### Results
#### 4.3.5. LFR Benchmark  
The LFR benchmark is a type of artificially generated network proposed by [PERSON] et al. in [33]. Compared to GN benchmark networks, LFR networks can simulate real-world networks more accurately in terms of its scale-free feature on degree  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **CN** & **NMI** \\ \hline Labeled partition & 2 & / \\ AMI-NRL & **2** & **0.72** \\ FN & 11 & 0.52 \\ Louvain & 4 & 0.63 \\ EdMot & 6 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of AMI-NRL and the other community detection algorithms on Polblogs.  
Figure 5: (**a**) AMI variation graph of adjacent partitions of Polblogs. (**b**) Vectors representing each node of Polblogs. (**c**) Result of community detection on Polblogs.  
information was used to find the most stable state in a community evolution process. The stability of each state of the process was measured by calculating the average mutual information of adjacent states and their information entropy. On the basis of this index, we proposed AMI-NRL. In this approach, the network is transformed into vectors through network representation learning. Then, agglomerative hierarchical clustering is performed on these vectors to simulate a real-world evolution process of communities. Finally, the optimal community partition can be found through the process by finding the peak value of the community evaluation index. Experiments on real-world and synthetic networks show that the approach is accurate and stable for the detection of communities.  
In experiments, we also found that the ground truth of some datasets was not necessarily the most reasonable community partition. By comparing the labeled partition of the Polbooks dataset and the partition obtained by AMI-NRL, we found that the community structure obtained by the latter was more closely connected within the community, while the connection between communities was sparser. In other words, its community structure was more explicit. In future research, we will analyze this issue in depth and try to propose a more accurate and reasonable community evaluation model.  
Due to the definition of AMI and the limitations of agglomerative hierarchical clustering, the current approach is limited to non-overlapping community detection in undirected and unweighted networks. We will continue to expand our research to more types of networks, as well as the detection of overlapping communities.  
Conceptualization, Y.C. and D.L.; methodology, Y.C.; software, Y.C.; validation, [PERSON]; investigation, C.W.; data curation, C.W. and D.L.; writing--original draft preparation, Y.C.; writing--review and editing, all authors.; visualization, Y.C.; supervision, D.L.; project administration, D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.