Understanding microbiome dynamics via interpretable graph representation learning

Abstract

This information can help in differentiating the microbiome profile of healthy and diseased individuals. Contribution.Overall, this paper makes the following contributions:• We present a model that learns a low-dimensional representation of the time-evolving graph in an unsupervised manner. Through our experiments, we demonstrate that the metastability governing the timeevolving graph is preserved in the new space.• We apply our method to real-world microbiome data to simplify the analysis of microbiome dynamics. This enables us to differentiate healthy and diseased states.• We also analyse the low-dimensional representation of the time-evolving graph throughout visualization and comparative analysis on the clustering task, where we show that our model outperforms other unsupervised graph representation learning methods.