PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning

Abstract

There has been rapid growth in biomedical literature, yet capturing the heterogeneity of the bibliographic information of these articles remains relatively understudied. Graph neural networks have gained popularity, however, they may not fully capture the information available in the PubMed database, a biomedical literature repository containing over 33 million articles. We introduce PubMed Graph Benchmark (PGB), a new benchmark dataset for evaluating heterogeneous graph representations. PGB is one of the largest heterogeneous networks to date and aggregates the rich metadata into a unified source including abstract, authors, citations, keywords, and the associated keyword hierarchy. The benchmark contains an evaluation task of 21 systematic review topics, an essential knowledge translation tool.