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arXiv · 2608.16455

Graph-Based Discovery of Mathematical Software Communities and Publication-to-Community Prediction

Maxence Azzouz-Thuderoz·Yuni Susanti·Moritz Schubotz
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Abstract

Research software forms distinct co-usage communities that span traditional disciplinary boundaries, yet the structure of these communities remains largely unexplored. We present a graph-based framework for discovering mathematical software communities and predicting their association with research publications. We construct a software co-usage network from publication-software relationships using a curated swMATH dataset and subsequently apply community detection method, revealing a heterogeneous landscape of mathematical software communities. We formulate publication-to-community mapping as a multi-label classification task and further investigate whether community membership can be predicted from lightweight scholarly metadata. Specifically, we compare two feature representations of scientific publications: Mathematics Subject Classification (MSC) and title-based embeddings. Across a range of models, structured MSC representation consistently provides a stronger precision-recall trade-off, demonstrating that structured domain metadata captures software-community structure more effectively than compressed title-only semantics in this setting. This work highlights the continuing value of structured scholarly metadata for large-scale research software discovery, classification and recommendation.

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Maxence Azzouz-Thuderoz, Yuni Susanti, Moritz Schubotz. 2026-08-17. Graph-Based Discovery of Mathematical Software Communities and Publication-to-Community Prediction. https://arxiv.org/abs/2608.16455

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