论文标题
基于机器学习的目标功能选择社区检测
Machine-Learning Based Objective Function Selection for Community Detection
论文作者
论文摘要
Nectar是一种以节点为中心的重叠社区检测算法，由Cohen等人于2016年提出。 al，根据调用其所调用的网络，在两个函数的两个目标函数之间动态选择。如Cohen等人所示，这种方法的表现优于六种最先进的算法，用于重叠的社区检测。在这项工作中，我们提出了Nectar-ML，这是花蜜算法的扩展，该算法使用基于机器学习的模型来自动选择目标函数的选择，在15,755合成和7个现实世界网络的数据集中进行训练和评估。我们的分析表明，在大约90％的情况下，我们的模型能够成功选择正确的目标函数。我们对Nectar和Nectar-ML进行了竞争分析。表明Nectar-ML显着胜过Nectar选择最佳目标函数的能力。我们还对花蜜ML和另外两种最先进的多目标社区检测算法进行了竞争分析。 Nectar-ML在平均检测质量方面优于这两种算法。多主体EAS（MOEAS）被认为是解决MOP的最流行方法，而Nectar-ML显着优于它们表明基于ML基于ML的目标函数选择的有效性。
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.
