  NECTAR, a Node-centric ovErlapping Community deTection AlgoRithm, presented
in 2016 by Cohen et. al, chooses dynamically between two objective functions
which function to optimize, based on the network on which it is invoked. This
approach, as shown by Cohen et al., outperforms six state-of-the-art algorithms
for overlapping community detection. In this work, we present NECTAR-ML, an
extension of the NECTAR algorithm that uses a machine-learning based model for
automating the selection of the objective function, trained and evaluated on a
dataset of 15,755 synthetic and 7 real-world networks. Our analysis shows that
in approximately 90% of the cases our model was able to successfully select the
correct objective function. We conducted a competitive analysis of NECTAR and
NECTAR-ML. NECTAR-ML was shown to significantly outperform NECTAR's ability to
select the best objective function. We also conducted a competitive analysis of
NECTAR-ML and two additional state-of-the-art multi-objective community
detection algorithms. NECTAR-ML outperformed both algorithms in terms of
average detection quality. Multiobjective EAs (MOEAs) are considered to be the
most popular approach to solve MOP and the fact that NECTAR-ML significantly
outperforms them demonstrates the effectiveness of ML-based objective function
selection.
