AI 中文总结
该研究提出基于图的框架，利用swMATH数据集构建软件共同使用网络以发现数学软件社区，将出版物到社区映射作为多标签分类任务，发现结构化MSC元数据在预测中表现更优，凸显其对研究软件相关工作的价值。
AI 中文摘要
研究软件形成了跨越传统学科边界的独特共同使用社区，但这些社区的结构在很大程度上仍未被探索。我们提出了一种基于图的框架，用于发现数学软件社区并预测它们与研究出版物的关联。我们使用经过整理的swMATH数据集，从出版物-软件关系中构建软件共同使用网络，随后应用社区检测方法，揭示了数学软件社区的异质格局。我们将出版物到社区的映射表述为多标签分类任务，并进一步研究是否可以从轻量级学术元数据中预测社区成员身份。具体而言，我们比较了科学出版物的两种特征表示：数学学科分类（MSC）和基于标题的嵌入。在一系列模型中，结构化MSC表示始终提供更优的精确率-召回率权衡，表明在该场景下，结构化领域元数据比仅压缩标题语义更有效地捕获软件-社区结构。这项工作凸显了结构化学术元数据对于大规模研究软件发现、分类和推荐的持续价值。
英文摘要
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.
