Recovering network topology and dynamics via sequence characterization
This addresses the challenge of understanding complex systems from limited sequence data, though it is incremental as it builds on existing co-occurrence methods.
The paper tackles the problem of recovering hidden network topology and agent dynamics from observed sequences, achieving 87% accuracy in classifying 16 combinations of topology and dynamics using sequences with less than 40% of nodes visited.
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.