HybridVis: An adaptive hybrid-scale visualization of multivariate graphs. (August 2017)

• Record Type: Journal Article
• Authors: Liu, Yuhua; Wang, Changbo; Ye, Peng; Zhang, Kang
• Is Part Of: Journal of visual languages & computing. Volume 41(2017)
• Page Start: 100
• Page End: 110
• Publication Date: 2017-08
• Subjects: Clustering -- Graph/network data -- Hierarchy data -- Focus + context techniques; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.03.008
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Existing network visualizations support hierarchical exploration, which rely on user interactions to create and modify graph hierarchies based on the patterns in the data attributes. It will take a relatively long time for users to identify the impact of different attributes on the cluster structure. To address this problem, this paper proposes a visual analytical approach, called HybridVis, creating an interactive layout to reveal clusters of obvious characteristics on one or more attributes at different scales. HybridVis can help people gain social insight and better understand the roles of attributes within a cluster. First, an approximate optimal graph hierarchy based on an energy model is created, considering both data attributes and relationships among data items. Then a layout algorithm and a level-dependent perceptual view for multi-scale graphs are proposed to show the attribute-driven graph hierarchy. Several views, which interact with each other, are designed in HybridVis, including a graphical view of the relationships among clusters; a cluster tree revealing the cluster scales and the details of attributes on parallel coordinates augmented with histograms and interactions. From the meaningful and globally approximate optimal abstraction, users can navigate a large multivariate graph with an overview+detail to explore and rapidly find the potential correlations between the graph structure and the attributes of data items. Finally, experiments using two real world data sets are performed to demonstrate the effectiveness of our methods.