Simbanex: Similarity-based Exploration of IEEE VIS Publications

TL;DR

Simbanex introduces a novel visual analytics tool that utilizes multiple embeddings and aspect-driven analysis to explore similarity patterns in large scientific publication datasets, enhancing bibliometric insights.

Contribution

The paper presents a new multivariate network approach and a similarity-based clustering method, integrated into an interactive visualization platform for scientific publications.

Findings

Effective similarity patterns revealed in publication data

Flexible aspect-driven embedding enables detailed analysis

Enhanced bibliometric exploration through visual analytics

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.