PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs

TL;DR

PanRep is a novel GNN framework that learns universal node embeddings for heterogeneous graphs, enabling improved performance in downstream tasks like classification and drug discovery, especially with limited labeled data.

Contribution

Introduces PanRep, a GNN-based unsupervised framework for universal node embeddings in heterogeneous graphs, with a multi-decoder design for broad applicability and fine-tuning capabilities.

Findings

Outperforms existing unsupervised and some supervised methods in node classification and link prediction.

Fine-tuned PanRep-FT surpasses all supervised approaches in performance.

Successfully applied to drug repurposing for Covid-19, identifying potential drug candidates.

Abstract

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.