Visual Analysis of Relationships between Heterogeneous Networks and Texts: An Application on the IEEE VIS Publication Dataset

摘要: The visual exploration of large and complex network structures remains a challenge for many application fields. Moreover, growing number real-world networks is multivariate often interconnected with each other. Entities in may have relationships elements other related datasets, which do not necessarily to be themselves, these defined by attributes that can vary greatly. In this work, we propose comprehensive analytics approach supports researchers specify subsequently explore attribute-based across networks, text documents derived secondary data. Our provides an individual search functionality based on keywords semantically similar terms over the entire corpus find nodes. For examining nodes views, introduce new interaction technique, called Hub2Go, facilitates navigation guiding user information interest. To showcase our system, use collected from research papers listed visualization publication dataset consists 2752 period 25 years. Here, analyze between various heterogeneous bag-of-words index word similarity matrix, all initial metadata.