Like before, we used 10 network instances for each kout. For each network and each SBM variant we executed the MHA 50k 10 times for each K = 1, …, 10. Then we considered one execution for each network for all number of group K ∈ {1, …, 10} as one unit and executed the model selection based on these results. Therefore, a data point is the average of 100 AMI values resulting from the 10 network instances and the 10 selected partitions. For the classic models only the best model selection according to Table 2 is shown.</p
<p>The clustering and gene selection results for the four set-ups with , in terms of the average fre...
<p><b>A</b>. Firing rate (sp/s) of 8 modular (red) and 8 uniform networks (black). The parameter is ...
<p>Results obtained in terms of average <i>Q</i><sup>2</sup> (across 500 replicates) for scenarios a...
Each marker represents the average of 10 network instances. For each network each of inference algor...
Like before, we used 10 network instances for each μt. For each network and each SBM variant we exec...
Each model beside the hierarchical ones (HSPC, HSDCPU, HDCPUH) was executed 10 times with MHA with 2...
Each marker represents the average of 10 network instances. For each network each of inference algor...
The resulting difference to the original value (designated with a blue dot ●), the average AMI of al...
Normalized AUMIC of the different SBM variants of the GN test for 0 ≤ kout ≤ 8 based on 10 execution...
In recent years, model selection methods have seen significant advancement, but improvements have te...
The effort to understand network systems in increasing detail has resulted in a diversity of methods...
Differences in AIC values, ΔAIC = AIC—AICmin, between the different models with 3 hidden states, whe...
The results shown are based on the same 10 executions for each of the 10 networks for each SBM varia...
To compute norms from reference group test scores, continuous norming is preferred over traditional ...
Model performance assessment given different sparsity distributions. Quantity of missing data varied...
<p>The clustering and gene selection results for the four set-ups with , in terms of the average fre...
<p><b>A</b>. Firing rate (sp/s) of 8 modular (red) and 8 uniform networks (black). The parameter is ...
<p>Results obtained in terms of average <i>Q</i><sup>2</sup> (across 500 replicates) for scenarios a...
Each marker represents the average of 10 network instances. For each network each of inference algor...
Like before, we used 10 network instances for each μt. For each network and each SBM variant we exec...
Each model beside the hierarchical ones (HSPC, HSDCPU, HDCPUH) was executed 10 times with MHA with 2...
Each marker represents the average of 10 network instances. For each network each of inference algor...
The resulting difference to the original value (designated with a blue dot ●), the average AMI of al...
Normalized AUMIC of the different SBM variants of the GN test for 0 ≤ kout ≤ 8 based on 10 execution...
In recent years, model selection methods have seen significant advancement, but improvements have te...
The effort to understand network systems in increasing detail has resulted in a diversity of methods...
Differences in AIC values, ΔAIC = AIC—AICmin, between the different models with 3 hidden states, whe...
The results shown are based on the same 10 executions for each of the 10 networks for each SBM varia...
To compute norms from reference group test scores, continuous norming is preferred over traditional ...
Model performance assessment given different sparsity distributions. Quantity of missing data varied...
<p>The clustering and gene selection results for the four set-ups with , in terms of the average fre...
<p><b>A</b>. Firing rate (sp/s) of 8 modular (red) and 8 uniform networks (black). The parameter is ...
<p>Results obtained in terms of average <i>Q</i><sup>2</sup> (across 500 replicates) for scenarios a...