  Graph Neural Networks (GNN) have been extensively used to extract meaningful
representations from graph structured data and to perform predictive tasks such
as node classification and link prediction. In recent years, there has been a
lot of work incorporating edge features along with node features for prediction
tasks. One of the main difficulties in using edge features is that they are
often handcrafted, hard to get, specific to a particular domain, and may
contain redundant information. In this work, we present a framework for
creating new edge features, applicable to any domain, via a combination of
self-supervised and unsupervised learning. In addition to this, we use
Forman-Ricci curvature as an additional edge feature to encapsulate the local
geometry of the graph. We then encode our edge features via a Set Transformer
and combine them with node features extracted from popular GNN architectures
for node classification in an end-to-end training scheme. We validate our work
on three biological datasets comprising of single-cell RNA sequencing data of
neurological disease, \textit{in vitro} SARS-CoV-2 infection, and human
COVID-19 patients. We demonstrate that our method achieves better performance
on node classification tasks over baseline Graph Attention Network (GAT) and
Graph Convolutional Network (GCN) models. Furthermore, given the attention
mechanism on edge and node features, we are able to interpret the cell types
and genes that determine the course and severity of COVID-19, contributing to a
growing list of potential disease biomarkers and therapeutic targets.
