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.

翻译：在许多现实世界情景中出现序列; 因此, 确定符号生成背后的机制对于理解许多复杂系统至关重要 。 本文分析了在网络地形学上行走的代理物产生的序列。 鉴于在许多真实情景中, 生成序列的基本过程被隐藏了, 我们调查通过共发方法重建网络是否有益于恢复网络地形学和产生序列的代理物动态。 我们发现, 重建后的网络的特征化为创建序列所使用的过程和结构学提供了宝贵的信息 。 在将网络地形学和代理物动态的16种组合作为分类的机器学习方法中, 我们获得了87%的准确率, 其生成的序列不到40%。 大的序列产生了更好的机器学习模型。 我们的研究结果表明, 拟议的方法可以扩展到对序列进行分类, 并了解序列生成背后的机制 。