# Detection of Hidden Communities in Twitter Discussions of Varying Volumes
## 5 Results  
As stated above, in this section we present the results of the experiment for four various datasets of different volumes. The results of the task solution for the Biryulevo case are shown in Tables 1 and 2. Each method has its own peculiarities:  
* The Infomap algorithm tends to highlight one large community, which can be used as a starting point for deeper research;
* Similar to the Infomap algorithm, GANXiS (both pure and overlapping versions) distinguishes one large society, but this set is smaller than for Infomap;
* The generalized \(K\)-means algorithm does not take into account the lack of connections between isolates, which is the reason for the unification of almost all small discussions (2-3-4 participants) into one large society;
* Directed Louvain shows good results. However, it was not able to get ahead in any metric, steadily holding on to the second or third place;
* Despite it only partially belonging to the directed graph clustering algorithms, Leiden (both the one that uses modularity and the one that uses the clique percolation method) shows good results, being ahead of the directed Louvain algorithm in almost every point. Its peculiarity is that it highlights the communities of average size and/or close to each other in size;
* DLPA allocates almost twice as many communities as other algorithms, all but the largest do not differ much in size;
* OSLOM identifies a large number of communities, which, moreover, can strongly overlap. The results for the Cologne case are shown in Tables 3 and 4. At this dataset volume, (3) metric showed high computational complexity, so it was no longer used. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **DirMod** & **ClusCoe** & **ComFit** & **NEDind** & **Conduc** & **Contrac** & **Expans** \\ \hline DLPA & 0.486 & **0.056** & 1.373 & 0.605 & 0.625 & **0.849** & 2.361 \\ DirLouv & 0.574 & 0.039 & 6.548 & 0.793 & 0.286 & 0.766 & 0.715 \\ Infomap & 0.088 & 0.034 & 7.877 & **0.940** & **0.209** & 0.685 & **0.375** \\ OSLOM & - & 0.035 & 2.479 & 0.501 & 0.967 & 0.087 & 1.403 \\ GKM & 0.390 & 0.009 & 7.**956** & 0.423 & 0.950 & 0.190 & 2.161 \\ LeidMod & **0.584** & 0.039 & 6.686 & 0.806 & 0.278 & 0.761 & 0.686 \\ LeidCPM & 0.571 & 0.040 & 5.052 & 0.676 & 0.365 & 0.828 & 1.001 \\ GANXiSd & 0.406 & 0.040 & 2.538 & 0.661 & 0.526 & 0.841 & 1.681 \\ GANXiSo & - & 0.049 & 3.295 & 0.643 & 0.550 & 0.848 & 2.282 \\ \hline \end{tabular} Note. The best results are highlighted in bold.  
\end{table} 
Table 1: Results of evaluations by metrics on the Biryulevo dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Count** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline DLPA & 1159 & 741 & 205 & 171 & 115 & 111 & 108 & 105 \\ DirLouv & 243 & 691 & 583 & 494 & 486 & 463 & 459 & 381 \\ Infomap & 202 & 9276 & 232 & 86 & 41 & 33 & 28 & 26 \\ OSLOM & 642 & 2637 & 1743 & 1425 & 847 & 763 & 737 & 619 \\ GKM & 200 & 3300 & 2815 & 1812 & 815 & 496 & 313 & 90 \\ LeidMod & 238 & 716 & 464 & 458 & 451 & 416 & 388 & 356 \\ LeidCPM & 315 & 627 & 613 & 226 & 226 & 214 & 208 & 208 \\ GANXiSd & 627 & 4881 & 190 & 146 & 129 & 113 & 103 & 101 \\ GANXiSo & 647 & 4956 & 638 & 255 & 166 & 146 & 139 & 83 \\ \hline \end{tabular}
\end{table} 
Table 2: The number of communities obtained on the Biryulevo dataset and the value of the seven largest of them for each algorithm. For the mid-range dataset, the algorithms have shown the following results:  
* Infomap showed a more even selection of communities than on the Biryulevo dataset, nevertheless retaining the emphasis on the largest of them;
* GANXiS continued to highlight one large community, and the emphasis on this community grew;
* As in the case of the Biryulevo dataset, generalized \(K\)-means has combined minor discussions into one large group;
* Directed Louvain performed well again, continuing to hold on to second or third place;
* Unlike for the first dataset, the sizes of communities after the Leiden algorithm differ depending on the metric (CPM or modularity).