Data specifications |
---|
dataset | vertices | edges | directed | self-loops | |
rfid | 75 | 32424 | False | False | |
karate | 34 | 231 | False | False | |
UKfaculty | 81 | 3730 | True | False | |
USairports | 755 | 23473 | True | True | |
enron | 184 | 125409 | True | True | |
Number of clusters |
dataset | fast_greedy | infomap | label_prop | spinglass | louvain |
rfid | 6 | 4 | 3 | 7 | 6 |
karate | 3 | 3 | 3 | 4 | 4 |
UKfaculty | 5 | 10 | 7 | 7 | 5 |
USairports | 28 | 57 | 40 | NA | 21 |
enron | 11 | 22 | 20 | NA | 10 |
\(\bf \Delta _{i}^{\text {AIC}{}}\) |
dataset | fast_greedy | infomap | label_prop | spinglass | louvain |
rfid | 1856 | 12370 | 13523 | 0 | 1856 |
karate | 28 | 28 | 28 | 4 | 0 |
UKfaculty | 992 | 0 | 960 | 523 | 992 |
USairports | 1903 | 2759 | 5133 | NA | 0 |
enron | 0 | 9881 | 46945 | NA | 1956 |
\(\bf \Delta _{i}^{\text {BIC}{}}\) |
dataset | fast_greedy | infomap | label_prop | spinglass | louvain |
rfid | 1798 | 12219 | 13339 | 0 | 1798 |
karate | 14 | 14 | 14 | 4 | 0 |
UKfaculty | 743 | 0 | 792 | 355 | 743 |
USairports | 3315 | 14227 | 9883 | NA | 0 |
enron | 0 | 11702 | 48347 | NA | 1849 |
- 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