Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree-table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.
@cite_48 introduce a radial layout for graphs based on spanning trees. A focus node is used as the root of a spanning tree and shown at the center, immediate neighbors are shown circling the focus nodes, neighbors once removed are shown on a second circle, etc. The edges of the spanning tree and other non-tree edges are shown in a different color. Animated transitions are used to dynamically update the focus node. MoireGraphs @cite_24 follow the same principle but combine the radial layout with rich on-node attribute visualizations.
@cite_24 @cite_48
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We describe a new animation technique for supporting interactive exploration of a graph. We use the well-known radial tree layout method, in which the view is determined by the selection of a focus node. Our main contribution is a method for animating the transition to a new layout when a new focus node is selected. In order to keep the transition easy to follow, the animation linearly interpolates the polar coordinates of the nodes, while enforcing ordering and orientation constraints. We apply this technique to visualizations of social networks and of the Gnutella file-sharing network, and discuss the results from our informal usability tests. University of TrierKeywords: Graph visualization, node-link diagrams, radial layout, splattingAbstract: We describe and discuss a novel radial version of a scalable dynamic graph visualization. The radial layoutencodes dynamic directed graphs on narrow rings of a circle. The temporal evolution of the graph is mapped torings that grow outward from the center of the circle. Graph vertices are placed equidistantly at the borderlinesof each ring. Graph edges are displayed as curved lines starting from a source on the inner borderline of thering and pointing to a target on the outer borderline. To better perceive link directions and structures of largedatasets, visual clutter is reduced by exploiting an edge splatting approach that generates density ﬁelds of thedisplayed edges. The radial layout emphasizes newer graphs, displayed in the larger, outer parts of the circle.As a beneﬁt, edge lengths are reduced in comparison to the non-radial visualization. Moreover, the radiallayout guarantees the symmetry of the visualization under shifting of vertices. We illustrate the usefulness ofthe diagrams by applying them to call graph data of the open source software project Cobertura. We present the H3 layout technique for drawing large directed graphs as node-link diagrams in 3D hyperbolic space. We can lay out much larger structures than can be handled using traditional techniques for drawing general graphs because we assume a hierarchical nature of the data. We impose a hierarchy on the graph by using domain-specific knowledge to find an appropriate spanning tree. Links which are not part of the spanning tree do not influence the layout but can be selectively drawn by user request. The volume of hyperbolic 3-space increases exponentially, as opposed to the familiar geometric increase of euclidean 3-space. We exploit this exponential amount of room by computing the layout according to the hyperbolic metric. We optimize the cone tree layout algorithm for 3D hyperbolic space by placing children on a hemisphere around the cone mouth instead of on its perimeter. Hyperbolic navigation affords a Focus+Context view of the structure with minimal visual clutter. We have successfully laid out hierarchies of over 20,000 nodes. Our implementation accommodates navigation through graphs too large to be rendered interactively by allowing the user to explicitly prune or expand subtrees. Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.