Uperior to the other algorithms as it displays a performance drop for a larger value of the mixing parameter . Importantly, the exact value of the mixing parameter of a graph is usually unobservable. To get a rough idea about the value of , one may employ either the Spinglass or the Multilevel algorithm. Limited by the computing time required, Spinglass algorithm cannot be applied on large networks. Based on the previous results, and taking into account both factors, accuracy and computing time, it is possible to suggest under which situations to use each algorithm depending sorely on topological properties of the network under study. Our PD173074MedChemExpress PD173074 recommendations for the use of community detection algorithms are summarised in Fig. 7. In the first region, ?0.5 and the network size is small, N 1000. There, most of the communities detection algorithms tested give accurate results (and the computing time is affordable): Infomap, Label propagation, Multilevel, Walktrap, Spinglass, and Edge betweenness can all be used in a trustworthy fashion. A second region has a relatively larger value of (0.5 ?0.6), and equally small sizes of network N 1000. There, it is possible to use Multilevel, Walktrap, and Spinglass algorithms. A third region encompasses again smaller values of mixing parameter (?0.5) but an intermediate number of nodes (1000 N 6000). In this region, the best choices are Infomap, label propagation, Multilevel, and Walktrap algorithms. With increasing number of nodes in the networks (6000 N 32000), Infomap and Multilevel algorithm are very likely to provide the wrong number of communities and therefore they are no longer suitable in the fourth region. The last region has the highest requirement for the community detection algorithms. None of the algorithms performs very well in this region but the Multilevel algorithm outperforms all the others. Besides, we illustrate the suggestion for the adaptive use of the DM-3189 clinical trials methods for community detection process in a simplified flow diagram (see Fig. 8). With any given network, one should first employ either Spinglass algorithm or Multilevel algorithm in order to obtain an estimate of the value of the mixing parameter . Notice that the former one can only be used for small networks (N 1000) due to the prohibitive computing time for larger network sizes. Second, one can choose a suitable method according to the values of N and to conduct the community detection such that both the accuracy and the computing time are acceptable. Third, as we have already shown, in certain situations, there might exist large standard deviations of NMI, i.e., the community detectionScientific RepoRts | 6:30750 | DOI: 10.1038/srepDiscussionwww.nature.com/scientificreports/Figure 6. (Lower row) The mean value of the computing time of the community detection algorithms (in seconds) dependent on the number of nodes in the benchmark graphs on a log-log scale. (upper row) The standard deviation of the computing time on a log-log scale. Different colours refer to different values of the mixing parameter: red ( = 0.03), green ( = 0.18), blue ( = 0.33), black ( = 0.48), cyan ( = 0.63), and purple ( = 0.75). Please notice that the vertical axis might have different scale ranges. Due to the computing speed, Spinglass and Edge betweenness algorithms have been tested only on networks with N 1000, and Infomap algorithm has been tested on networks with N 22186. The other parameters are described in Table 1.algorithms are not stable and t.Uperior to the other algorithms as it displays a performance drop for a larger value of the mixing parameter . Importantly, the exact value of the mixing parameter of a graph is usually unobservable. To get a rough idea about the value of , one may employ either the Spinglass or the Multilevel algorithm. Limited by the computing time required, Spinglass algorithm cannot be applied on large networks. Based on the previous results, and taking into account both factors, accuracy and computing time, it is possible to suggest under which situations to use each algorithm depending sorely on topological properties of the network under study. Our recommendations for the use of community detection algorithms are summarised in Fig. 7. In the first region, ?0.5 and the network size is small, N 1000. There, most of the communities detection algorithms tested give accurate results (and the computing time is affordable): Infomap, Label propagation, Multilevel, Walktrap, Spinglass, and Edge betweenness can all be used in a trustworthy fashion. A second region has a relatively larger value of (0.5 ?0.6), and equally small sizes of network N 1000. There, it is possible to use Multilevel, Walktrap, and Spinglass algorithms. A third region encompasses again smaller values of mixing parameter (?0.5) but an intermediate number of nodes (1000 N 6000). In this region, the best choices are Infomap, label propagation, Multilevel, and Walktrap algorithms. With increasing number of nodes in the networks (6000 N 32000), Infomap and Multilevel algorithm are very likely to provide the wrong number of communities and therefore they are no longer suitable in the fourth region. The last region has the highest requirement for the community detection algorithms. None of the algorithms performs very well in this region but the Multilevel algorithm outperforms all the others. Besides, we illustrate the suggestion for the adaptive use of the methods for community detection process in a simplified flow diagram (see Fig. 8). With any given network, one should first employ either Spinglass algorithm or Multilevel algorithm in order to obtain an estimate of the value of the mixing parameter . Notice that the former one can only be used for small networks (N 1000) due to the prohibitive computing time for larger network sizes. Second, one can choose a suitable method according to the values of N and to conduct the community detection such that both the accuracy and the computing time are acceptable. Third, as we have already shown, in certain situations, there might exist large standard deviations of NMI, i.e., the community detectionScientific RepoRts | 6:30750 | DOI: 10.1038/srepDiscussionwww.nature.com/scientificreports/Figure 6. (Lower row) The mean value of the computing time of the community detection algorithms (in seconds) dependent on the number of nodes in the benchmark graphs on a log-log scale. (upper row) The standard deviation of the computing time on a log-log scale. Different colours refer to different values of the mixing parameter: red ( = 0.03), green ( = 0.18), blue ( = 0.33), black ( = 0.48), cyan ( = 0.63), and purple ( = 0.75). Please notice that the vertical axis might have different scale ranges. Due to the computing speed, Spinglass and Edge betweenness algorithms have been tested only on networks with N 1000, and Infomap algorithm has been tested on networks with N 22186. The other parameters are described in Table 1.algorithms are not stable and t.