# Detection of Hidden Communities in Twitter Discussions of Varying Volumes
## 5 Results
The first option showed a large number of fairly small communities, while the second showed a smaller number of moderately large communities;
* DLPA on the Cologne dataset showed the risk of going into \"overflow\" of one community, not giving any other one chances to grow while, again, having twice as many communities as other algorithms;
* OSLOM distinguished communities of not the best quality even in comparison with GANXiS. The results for the large dataset (Tables 5 and 6) may be described the following way:
* Infomap once again got four best metrics results, receiving, though, one large community;
* GANXiS kept the trend shown on small and medium datasets;
* The generalized \(K\)-Means algorithm has shown its inapplicability to sufficiently perform on large graphs, since both the computational cost and the memory cost made it impossible to use this algorithm;
* Directed Louvain retains its position relative to other algorithms;
* The Leiden algorithm kept the trend shown on the medium-sized dataset;
* DLPA identified more equal-sized communities than on the medium dataset, which demonstrates variability of the algorithm performance depending on the size of initial data;
* OSLOM got results worse than on medium dataset on every, except metric (8). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Count** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline DLPA & 2479 & 20,669 & 425 & 277 & 249 & 198 & 173 & 168 \\ DirLouv & 735 & 4356 & 4105 & 4091 & 3604 & 3447 & 1933 & 1886 \\ Infomap & 673 & 27,016 & 3726 & 1145 & 590 & 465 & 445 & 338 \\ OSLOM & 1690 & 10,823 & 7336 & 3058 & 1881 & 1519 & 1466 & 921 \\ GKM & 200 & 11,474 & 7737 & 4353 & 2971 & 2947 & 1859 & 1119 \\ LeidMod & 734 & 4453 & 3882 & 3540 & 3212 & 3181 & 2686 & 1739 \\ LeidCPM & 1221 & 2395 & 672 & 625 & 414 & 405 & 362 & 355 \\ GANXiSd & 1864 & 24,911 & 128 & 110 & 108 & 94 & 89 & 80 \\ GANXiSo & 1964 & 25365 & 458 & 210 & 162 & 128 & 110 & 83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The number of communities obtained on the Cologne dataset and the value of the seven largest of them for each algorithm. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **DirMod** & **ClusCoe** & **ComFit** & **NEDind** & **Conduc** & **Contrac** & **Expans** \\ \hline DLPA & - & **0.070** & 2.299 & 0.723 & 0.521 & 0.815 & 1.378 \\ DirLouv & - & 0.028 & 7.753 & 0.917 & 0.234 & 0.691 & 0.459 \\ Infomap & - & 0.024 & 8.468 & **0.949** & **0.201** & 0.653 & **0.344** \\ OSLOM & - & 0.034 & 3.380 & 0.495 & 0.963 & 0.098 & 1.249 \\ GKM & - & 0.013 & **28.498** & 0.422 & 0.934 & 0.209 & 2.600 \\ LeidMod & - & 0.028 & 7.764 & 0.916 & 0.233 & 0.693 & 0.459 \\ LeidCPM & - & 0.033 & 4.379 & 0.689 & 0.397 & 0.793 & 1.030 \\ GANXiSd & - & 0.031 & 3.057 & 0.693 & 0.501 & 0.807 & 1.798 \\ GANXiSo & - & 0.042 & 3.551 & 0.670 & 0.526 & **0.829** & 2.290 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of evaluations by metrics on the Cologne dataset. The results for the extra-large dataset (Tables 7 and 8) are the following:  
* Infomap showed a better division into same-size communities, retaining the emphasis on the largest of them;
* It revealed that the GANXiS algorithm is not applicable to extra-large networks;
* As in the case of the large dataset, generalized \(K\)-means is not applicable to the networks of this size;
* The directed Louvain continued to evenly allocate communities;
* Leiden shown dramatically grown difference between the size of CPM and modularity metric-based communities;
* DLPA on an extra-large dataset has allocated a lot of small, smaller than before, communities;
* OSLOM has got even worse results than on the large dataset on every metric.