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

• URL: http://arxiv.org/abs/2608.16455v1
• Date: Mon, 17 Aug 2026 11:56:08 GMT
• Title: Graph-Based Discovery of Mathematical Software Communities and Publication-to-Community Prediction
• Abstract summary: Research software forms distinct co-usage communities that span traditional disciplinary boundaries.<n>We present a graph-based framework for discovering mathematical software communities and predicting their association with research publications.
• Score: 2.006352089238062
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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