Simbanex: Similarity-based Exploration of IEEE VIS Publications
This work addresses the need for better tools in bibliometrics and scientometrics to analyze large sets of publications, though it is incremental as it builds on existing embedding and clustering methods.
The authors tackled the problem of exploring similarity patterns in scientific publications by developing Simbanex, a visual analytics application that uses multivariate networks and aspect-driven embeddings for similarity-based clustering, resulting in an interactive tool for bibliometric analysis.
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.