论文标题
通过序列表征恢复网络拓扑和动态
Recovering network topology and dynamics via sequence characterization
论文作者
论文摘要
序列在许多实际情况下出现。因此，识别符号生成背后的机制对于理解许多复杂系统至关重要。本文分析了在网络拓扑上行走的代理产生的序列。鉴于在许多实际情况下，生成序列的基础过程是隐藏的，我们研究了通过共发生方法重建网络是否有助于恢复网络拓扑和代理动力学生成序列。我们发现，重建网络的表征提供了有关用于创建序列的过程和拓扑的有价值的信息。在考虑16种网络拓扑和代理动力学组合组合的机器学习方法中，我们获得了87％的精度，序列生成的序列少于访问的节点的少于40％。事实证明，较大的序列可以生成改进的机器学习模型。我们的发现表明，可以扩展所提出的方法以对序列进行分类并了解序列产生背后的机制。
Sequences arise in many real-world scenarios; thus, identifying the mechanisms behind symbol generation is essential to understanding many complex systems. This paper analyzes sequences generated by agents walking on a networked topology. Given that in many real scenarios, the underlying processes generating the sequence is hidden, we investigate whether the reconstruction of the network via the co-occurrence method is useful to recover both the network topology and agent dynamics generating sequences. We found that the characterization of reconstructed networks provides valuable information regarding the process and topology used to create the sequences. In a machine learning approach considering 16 combinations of network topology and agent dynamics as classes, we obtained an accuracy of 87% with sequences generated with less than 40% of nodes visited. Larger sequences turned out to generate improved machine learning models. Our findings suggest that the proposed methodology could be extended to classify sequences and understand the mechanisms behind sequence generation.
