论文标题
通过复杂的网络结构预测选民模型意见的机器学习
Machine learning for the prediction of voter model opinions through complex network structures
论文作者
论文摘要
从系统的结构特征中的动态过程中的结果推论是网络科学中的至关重要的努力。最近的研究提出了一种基于机器学习的方法，用于解释复杂网络系统中出现的动态模式。该假设在这项研究中应用于显示可以通过使用复杂网络的拓扑特征在选民模型中归类的意见。首先通过2（0或1）意见进行了分析，并从选民模型中提出了3个分析，然后延长了3个观点，然后从同一模型中提出了4个意见。提供了估计的关键网络拓扑特征的细分，并且网络指标以很高的精度按重要性排名。我们的广义方法适用于在复杂网络上运行的动态过程。这项研究是将机器学习方法应用于复杂网络系统动态模式的研究的一步。
The inference of outcomes in dynamic processes from structural features of systems is a crucial endeavor in network science. Recent research has suggested a machine learning-based approach for the interpretation of dynamic patterns emerging in complex network systems. The hypothesis is applied in this study towards showing opinions can be classified in the voter model with the use of topological features from complex networks. An analysis was first performed with 2 (0 or 1) opinions from a voter model and extended for 3 and then 4 opinions from the same model. A breakdown of the key network topological features for the estimation is provided and network metrics are ranked in the order of importance with high accuracy. Our generalized approach is applicable to dynamical processes running on complex networks. This study is a step towards the application of machine learning methods to studies of dynamical patterns from complex network systems.
