De Bruijn

De Bruijn sequences and graphs are combinatorial structures finding increasing application in diverse fields, primarily focused on efficiently representing and analyzing sequential data. Current research emphasizes leveraging De Bruijn graphs within neural network architectures, particularly graph neural networks (GNNs), to capture both structural and contextual information in sequences, improving tasks like genomic sequence representation and time series analysis on dynamic graphs. These advancements are driven by the need for robust methods to handle increasingly large and complex datasets, with applications ranging from bioinformatics to structured light coding and beyond.