  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.
