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Informatics 2017, 4(2), 11; doi:10.3390/informatics4020011

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

1
Department of Computer Science, Linnaeus University, 351 95 Växjö, Sweden
2
Swedish Research Institute (RISE SICS), Box 1263, 164 29 Kista, Sweden
*
Author to whom correspondence should be addressed.
Academic Editors: Achim Ebert and Gunther H. Weber
Received: 6 April 2017 / Revised: 28 April 2017 / Accepted: 8 May 2017 / Published: 11 May 2017
(This article belongs to the Special Issue Scalable Interactive Visualization)
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Abstract

The visual exploration of large and complex network structures remains a challenge for many application fields. Moreover, a growing number of real-world networks is multivariate and often interconnected with each other. Entities in a network may have relationships with elements of other related datasets, which do not necessarily have to be networks themselves, and these relationships may be defined by attributes that can vary greatly. In this work, we propose a comprehensive visual analytics approach that supports researchers to specify and subsequently explore attribute-based relationships across networks, text documents and derived secondary data. Our approach provides an individual search functionality based on keywords and semantically similar terms over the entire text corpus to find related network nodes. For examining these nodes in the interconnected network views, we introduce a new interaction technique, called Hub2Go, which facilitates the navigation by guiding the user to the information of interest. To showcase our system, we use a large text corpus collected from research papers listed in the visualization publication dataset that consists of 2752 documents over a period of 25 years. Here, we analyze relationships between various heterogeneous networks, a bag-of-words index and a word similarity matrix, all derived from the initial corpus and metadata. View Full-Text
Keywords: heterogeneous networks; interaction; graph drawing; multivariate datasets; NLP; text analysis; visualization; visual analytics heterogeneous networks; interaction; graph drawing; multivariate datasets; NLP; text analysis; visualization; visual analytics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zimmer, B.; Sahlgren, M.; Kerren, A. Visual Analysis of Relationships between Heterogeneous Networks and Texts: An Application on the IEEE VIS Publication Dataset. Informatics 2017, 4, 11.

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