用几何方法融合文本与作者网络，捕捉研究主题随时间的演化轨迹。
A Multiscale Geometric Method for Capturing Relational Topic Alignment
- 结合文本与作者网络，用希尔伯特距离构建层次化主题树
- 有效识别稀有主题并展现主题演化的平滑过程
- 适合关注科研趋势演变的研究者使用
可解释的主题建模对于追踪合作者群体中研究兴趣的演变至关重要。在崇尚创新的科学语料库中，识别未充分代表的细分主题尤为重要。然而，基于密集Transformer嵌入的现代模型往往忽略罕见主题，难以捕捉平滑的时间对齐。我们提出一种几何方法，整合多模态文本与作者网络数据，利用希尔伯特距离和Ward聚类构建层次化主题树状图。该方法同时捕捉局部与全局结构，支持语义与时间维度上的多尺度学习。实验表明，该方法能有效识别稀有主题结构，并可视化主题随时间的平滑迁移。结果凸显了可解释的词袋模型与严谨几何对齐结合的优势。
原文摘要 · Abstract (English)
Interpretable topic modeling is essential for tracking how research interests evolve within co-author communities. In scientific corpora, where novelty is prized, identifying underrepresented niche topics is particularly important. However, contemporary models built from dense transformer embeddings tend to miss rare topics and therefore also fail to capture smooth temporal alignment. We propose a geometric method that integrates multimodal text and co-author network data, using Hellinger distances and Ward's linkage to construct a hierarchical topic dendrogram. This approach captures both local and global structure, supporting multiscale learning across semantic and temporal dimensions. Our method effectively identifies rare-topic structure and visualizes smooth topic drift over time. Experiments highlight the strength of interpretable bag-of-words models when paired with principled geometric alignment.
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