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Classifying research output into context-specific label taxonomies is achallenging and relevant downstream task, given the volume of existing andnewly published articles. We propose a method to enhance the performance ofarticle classification by enriching simple Graph Neural Network (GNN) pipelineswith multi-graph representations that simultaneously encode multiple signals ofarticle relatedness, e.g. references, co-authorship, shared publication source,shared subject headings, as distinct edge types. Fully supervised transductivenode classification experiments are conducted on the Open Graph BenchmarkOGBN-arXiv dataset and the PubMed diabetes dataset, augmented with additionalmetadata from Microsoft Academic Graph and PubMed Central, respectively. Theresults demonstrate that multi-graphs consistently improve the performance of avariety of GNN models compared to the default graphs. When deployed with SOTAtextual node embedding methods, the transformed multi-graphs enable simple andshallow 2-layer GNN pipelines to achieve results on par with more complexarchitectures.

Article Classification with Graph Neural Networks and Multigraphs