Machine-Learning Based Objective Function Selection for Community Detection
This work addresses the challenge of automating objective function selection for community detection, an incremental improvement over prior methods.
The paper tackles the problem of selecting objective functions for overlapping community detection in networks by extending the NECTAR algorithm with a machine-learning model, NECTAR-ML, which correctly selects the function in about 90% of cases and outperforms existing algorithms in detection quality.
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.