Treatment individualization and adaptation over time are crucial for managing chronic diseases.

Inthiswork,wepropose S3GCwhich uses contrastive learning along with Graph Neural Networks and node features to learn clusterable features. We empirically demonstrate that S3GC is able to learn the correct cluster structure evenwhen graph information ornode features are individually not informative enough to learn correct clusters.

DeepICLR (Poster), 2(3):4, [54] Yang Wang, inrealnetworks:22nd International Distributed, pages 25-34.