  Classifying research output into context-specific label taxonomies is a
challenging and relevant downstream task, given the volume of existing and
newly published articles. We propose a method to enhance the performance of
article classification by enriching simple Graph Neural Network (GNN) pipelines
with multi-graph representations that simultaneously encode multiple signals of
article relatedness, e.g. references, co-authorship, shared publication source,
shared subject headings, as distinct edge types. Fully supervised transductive
node classification experiments are conducted on the Open Graph Benchmark
OGBN-arXiv dataset and the PubMed diabetes dataset, augmented with additional
metadata from Microsoft Academic Graph and PubMed Central, respectively. The
results demonstrate that multi-graphs consistently improve the performance of a
variety of GNN models compared to the default graphs. When deployed with SOTA
textual node embedding methods, the transformed multi-graphs enable simple and
shallow 2-layer GNN pipelines to achieve results on par with more complex
architectures.
