Simbanex: Similarity-based Exploration of IEEE VIS Publications

Daniel Witschard, Ilir Jusufi, Andreas Kerren
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Abstract

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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Simbanex:基于相似性的 IEEE VIS 出版物探索
嵌入式是将复杂和非结构化数据转换为适合计算分析任务的数字格式的强大工具。在这项工作中,我们使用多重嵌入进行相似性计算,并将其应用于文献计量学和科学计量学。我们从大量科学出版物中建立了一个多变量网络(MVN),并探索了一种方面驱动的分析方法,以揭示给定出版物数据中的相似性模式。通过将多变量网络划分为可单独嵌入的方面,我们能够获得灵活的向量表示,并将其作为基于相似性聚类的新方法的输入。在这些预处理步骤的基础上,我们开发了名为 Simbanex 的可视化分析应用,该应用旨在对底层出版物中的相似性模式进行交互式可视化探索。
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