# SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications

arXiv: https://arxiv.org/abs/2204.00880 · HTML: https://mlsanity.com/paper/2204.00880/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 2 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020)
- [A Web-scale system for scientific knowledge exploration](https://mlsanity.com/paper/1805.12216/) (2018)
- [OpenCitations, an infrastructure organization for open scholarship](https://mlsanity.com/paper/1906.11964/) (2019)
- [Large Scale Subject Category Classification of Scholarly Papers with Deep Attentive Neural Networks](https://mlsanity.com/paper/2007.13826/) (2020)
- [BIP! DB: A Dataset of Impact Measures for Scientific Publications](https://mlsanity.com/paper/2101.12001/) (2021)

## Built on by (most cited first, 2 of 2)

- [Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes](https://mlsanity.com/paper/2410.05770/) (2024)
- [Research Entity Extraction and Topic Detection from UKRI Grant Proposals](https://mlsanity.com/paper/2606.30304/) (2026)

## Abstract (arXiv)

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 more effectively. Most existing works address classification either at venue level or solely based on the textual content of a research publication. We present SciNoBo, a novel classification system of publications to predefined FoS taxonomies, leveraging the structural properties of a publication and its citations and references organised in a multilayer network. In contrast to other works, our system supports assignments of publications to multiple fields by considering their multidisciplinarity potential. By unifying publications and venues under a common multilayer network structure made up of citing and publishing relationships, classifications at the venue-level can be augmented with publication-level classifications. We evaluate SciNoBo on a publications' dataset extracted from Microsoft Academic Graph and we perform a comparative analysis against a state-of-the-art neural-network baseline. The results reveal that our proposed system is capable of producing high-quality classifications of publications.