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To answer these questions, we first detect communities onto the networks by means of three different detection algorithms.
This work allows for the first time a comparison of different detection algorithms at a survey scale accounting for both planet completeness and false-positive rate.
Figure 15 Overall computational complexity of a 4×4 16-QAM system of different detection algorithms.
Choosing different detection algorithms or sensing parameters leads to different ROCs.
Figure 5 shows the computational complexity versus the number of user classes for different detection algorithms.
Figure 12 Complexity of a 4×4 16-QAM system for different detection algorithms.
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It should be noted that different community detection algorithms yield sufficiently different clustering coefficient values including large values (Fig. S5(a)).
The results also indicate that there is no much difference between the prior got by different community detection algorithms.
Three different novelty detection algorithms are considered here: outlier analysis, density estimation and an auto-associative neural network technique.
The proposed APF removes the most leading harmonics by using two different current detection algorithms and the obtained results are compared in terms of current THD level and the power factor.
The above results motivate the choice of three different community detection algorithms.
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