Recovering network topology and dynamics via sequence characterization

TL;DR

This paper explores how analyzing sequences generated by agents on networks can help recover the underlying network topology and dynamics, using a co-occurrence method and machine learning, with promising accuracy even with limited data.

Contribution

It introduces a novel approach combining sequence characterization and machine learning to infer network structure and agent behavior from sequence data.

Findings

Achieved 87% accuracy in classifying network-dynamics combinations.

Larger sequences improve the machine learning model's performance.

Reconstructed network characterization provides insights into the sequence generation process.

Abstract

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.