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Classifying scientific publications according to Field-of-Science (FoS)taxonomies is of crucial importance, allowing funders, publishers, scholars,companies and other stakeholders to organize scientific literature moreeffectively. Most existing works address classification either at venue levelor solely based on the textual content of a research publication. We presentSciNoBo, a novel classification system of publications to predefined FoStaxonomies, leveraging the structural properties of a publication and itscitations and references organised in a multilayer network. In contrast toother works, our system supports assignments of publications to multiple fieldsby considering their multidisciplinarity potential. By unifying publicationsand venues under a common multilayer network structure made up of citing andpublishing relationships, classifications at the venue-level can be augmentedwith publication-level classifications. We evaluate SciNoBo on a publications'dataset extracted from Microsoft Academic Graph and we perform a comparativeanalysis against a state-of-the-art neural-network baseline. The results revealthat our proposed system is capable of producing high-quality classificationsof publications.

SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications