Authors:
Ilir Jusufi
1
;
Andreas Kerren
2
;
Jiayi Liu
2
and
Björn Zimmer
2
Affiliations:
1
Linnaeus University, University of California and Davis, Sweden
;
2
Linnaeus University, Sweden
Keyword(s):
Network Visualization, Multivariate Data, Clustering, Document Visualization, Text Visualization, Interaction, Visual Analytics.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Graph Visualization
;
Information and Scientific Visualization
;
Interface and Interaction Techniques for Visualization
;
Text and Document Visualization
;
Visual Representation and Interaction
;
Visualization Applications
Abstract:
The visualization of networks with additional attributes attached to the network elements is one of the ongoing challenges in the information visualization domain. Such so-called multivariate networks regularly appear in various application fields, for instance, in data sets which describe friendship networks or co-authorship networks. Here, we focus on networks that are based on text documents, i.e., the network nodes represent documents and the edges show relationships between them. Those relationships can be derived from common topics or common co-authors. Attached attributes may be specific keywords (topics), keyword frequencies, etc. The analysis of such multivariate networks is challenging, because a deeper understanding of the data provided depends on effective visualization and interaction techniques that are able to bring all types of information together. In addition, automatic analysis methods should be used to support the analysis process of potentially large amounts of d
ata. In this paper, we present a visualization approach that tackles those analysis problems. Our implementation provides a combination of new techniques that shows intra-cluster and inter-cluster relations while giving insight into the content of the cluster attributes. Hence, it facilitates the interactive exploration of the networks under consideration by showing the relationships between node clusters in context of network topology and multivariate attributes.
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