用多维度网络分析论文相似性，交互式探索科研文献模式
Simbanex: Similarity-based Exploration of IEEE VIS Publications
- 构建多方面科学论文网络，分维度嵌入表示
- 提出基于相似性的聚类方法，揭示文献间隐藏关联
- 开发交互式可视化工具，适合科研趋势洞察者
嵌入技术能将复杂非结构化数据转化为数值形式，便于计算分析。本文利用多种嵌入方法进行相似性计算，应用于文献计量与科学计量研究。我们从大量科学出版物中构建了一个多变量网络（MVN），并采用面向特定方面的分析方法，揭示出版物数据中的相似性模式。通过将MVN分解为可独立嵌入的方面，获得灵活的向量表示，并以此为基础提出一种新颖的基于相似性的聚类方法。在此基础上，开发了名为Simbanex的可视化分析应用，支持对底层出版物中相似性模式的交互式探索。
原文摘要 · Abstract (English)
Embeddings are powerful tools for transforming complex and unstructured data into numeric formats suitable for computational analysis tasks. In this work, we use multiple embeddings for similarity calculations to be applied in bibliometrics and scientometrics. We build a multivariate network (MVN) from a large set of scientific publications and explore an aspect-driven analysis approach to reveal similarity patterns in the given publication data. By dividing our MVN into separately embeddable aspects, we are able to obtain a flexible vector representation which we use as input to a novel method of similarity-based clustering. Based on these preprocessing steps, we developed a visual analytics application, called Simbanex, that has been designed for the interactive visual exploration of similarity patterns within the underlying publications.
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