# Recovering network topology and dynamics via sequence characterization

arXiv: https://arxiv.org/abs/2206.15190 · HTML: https://mlsanity.com/paper/2206.15190/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 55 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [Patterns of authors contribution in scientific manuscripts](https://mlsanity.com/paper/1609.05545/) (2016)
- [A systematic comparison of supervised classifiers](https://mlsanity.com/paper/1311.0202/) (2013)
- [Complex systems: features, similarity and connectivity](https://mlsanity.com/paper/1606.05400/) (2016)
- [Mapping computational thinking mindsets between educational levels with cognitive network science](https://mlsanity.com/paper/2007.09402/) (2020)
- [Authorship Attribution Based on Life-Like Network Automata](https://mlsanity.com/paper/1610.06498/) (2016)
- [On the role of words in the network structure of texts: application to authorship attribution](https://mlsanity.com/paper/1705.04187/) (2017)
- [A comparative analysis of knowledge acquisition performance in complex networks](https://mlsanity.com/paper/2007.12028/) (2020)
- [Characteristic times of biased random walks on complex networks](https://mlsanity.com/paper/1307.3430/) (2013)
- [Using Complex Networks to Quantify Consistency in the Use of Words](https://mlsanity.com/paper/1302.4107/) (2013)
- [Knowledge Acquisition: A Complex Networks Approach](https://mlsanity.com/paper/1703.03366/) (2017)
- [Connecting Network Science and Information Theory](https://mlsanity.com/paper/1704.03091/) (2017)
- [The Dynamics of Knowledge Acquisition via Self-Learning in Complex Networks](https://mlsanity.com/paper/1802.09337/) (2018)

## Abstract (arXiv)

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.