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Besides the analysis of user-created groups, the study of automatically detected groups through community detection algorithms has attracted much interest lately [55].
Also, the comparison of the structure and sociality and topicality traits between declared groups and groups from community detection algorithms reveals that detected groups do not overlap much with declared groups on average, but they match sensibly more than the random case for groups of comparable sizes.
In addition to user-created groups (we refer to them as declared), in Section 5.1 we analyze the sociality and topicality properties of groups that are not defined by users but are instead found by community detection algorithms (we name these detected groups).
The activity drop for detected groups is continuous and much more moderate (Figure 8(f)), since community detection algorithms tend by design to output node clusters with high numbers of connections between them.
Consequently, the similarity of detected groups to the best-matching declared groups is 0.082, while for the randomized detected groups it is not much lower, yielding 0.058.
The Kappa value for detected groups is around 0.44, revealing lower agreement.
Similar(7)
Similarity is computed for all declared-detected group pairs and for each detected group we select the declared one with the highest similarity value as the best match.
"No viruses detected" group refers to patients in whom no respiratory virus was detected in the respiratory secretions, serum or the ventilator filters.
"Airborne viruses detected" group refers to patients in whom a new respiratory virus was detected in the inspiratory filter or the expiratory filter.
The patients in all studies were classified into one of four groups as follows: both endotoxemia and GN bacteremia detected (group one), only GN bacteremia detected (group two), only endotoxemia detected (group three), and neither detected (group four).
P-values of imbalances detected, grouped by genotype availability and accuracy.
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