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Table 5 Results of the fitting of BCCM to five real-world graphs, with vertex blocks given obtained from five different community detection algorithms

From: The block-constrained configuration model

Data specifications
datasetverticesedgesdirectedself-loops 
rfid7532424FalseFalse 
karate34231FalseFalse 
UKfaculty813730TrueFalse 
USairports75523473TrueTrue 
enron184125409TrueTrue 
Number of clusters
datasetfast_greedyinfomaplabel_propspinglasslouvain
rfid64376
karate33344
UKfaculty510775
USairports285740NA21
enron112220NA10
\(\bf \Delta _{i}^{\text {AIC}{}}\)
datasetfast_greedyinfomaplabel_propspinglasslouvain
rfid1856123701352301856
karate28282840
UKfaculty9920960523992
USairports190327595133NA0
enron0988146945NA1956
\(\bf \Delta _{i}^{\text {BIC}{}}\)
datasetfast_greedyinfomaplabel_propspinglasslouvain
rfid1798122191333901798
karate14141440
UKfaculty7430792355743
USairports3315142279883NA0
enron01170248347NA1849
  1. The first table reports information about the five different graphs used. The second table reports the number of clusters detected by each algorithm for each dataset. The algorithm detecting the smallest number of clusters is highlighted in bold, and the algorithm detecting the largest number of clusters is highlighted in italic. The third table reports AIC differences of the different models computed using the different vertex blocks. The fourth table reports BIC differences of the different models computed using the different vertex blocks. The best model, i.e., the one with the lowest AIC/BIC score, respectively, is highlighted in bold. Because the spin-glass algorithm is not suitable for disconnected graphs, no result is reported for this method for the last two real-world graphs