Networks of motifs from sequences of symbols

TL;DR

This paper presents a novel method to transform sequences of symbols into weighted directed networks of motifs, enabling analysis of complex data across biological, social, and dynamical systems.

Contribution

The paper introduces a new network-based approach to analyze symbol sequences by identifying motifs and their significant co-occurrences, facilitating diverse applications.

Findings

Networks of motifs can correlate sequences with biological functions.

Method detects hot topics in social dialogs.

Characterizes trajectories of dynamical systems.

Abstract

We introduce a method to convert an ensemble of sequences of symbols into a weighted directed network whose nodes are motifs, while the directed links and their weights are defined from statistically significant co-occurences of two motifs in the same sequence. The analysis of communities of networks of motifs is shown to be able to correlate sequences with functions in the human proteome database, to detect hot topics from online social dialogs, to characterize trajectories of dynamical systems, and might find other useful applications to process large amount of data in various fields.