  Learning unsupervised node embeddings facilitates several downstream tasks
such as node classification and link prediction. A node embedding is universal
if it is designed to be used by and benefit various downstream tasks. This work
introduces PanRep, a graph neural network (GNN) model, for unsupervised
learning of universal node representations for heterogenous graphs. PanRep
consists of a GNN encoder that obtains node embeddings and four decoders, each
capturing different topological and node feature properties. Abiding to these
properties the novel unsupervised framework learns universal embeddings
applicable to different downstream tasks. PanRep can be furthered fine-tuned to
account for possible limited labels. In this operational setting PanRep is
considered as a pretrained model for extracting node embeddings of heterogenous
graph data. PanRep outperforms all unsupervised and certain supervised methods
in node classification and link prediction, especially when the labeled data
for the supervised methods is small. PanRep-FT (with fine-tuning) outperforms
all other supervised approaches, which corroborates the merits of pretraining
models. Finally, we apply PanRep-FT for discovering novel drugs for Covid-19.
We showcase the advantage of universal embeddings in drug repurposing and
identify several drugs used in clinical trials as possible drug candidates.
