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.
The works most closely related to ours are TreePlus by @cite_41 and the application-specific variant of TreePlus, GOTreePlus @cite_19 . TreePlus introduces the plant a seed and watch it grow'' principle. Based on an initial, user-chosen node, analysts can grow the spanning tree by successively revealing subtrees. TreePlus shows hidden links between the tree nodes on demand using a combination of highlighting, a separate view of neighboring nodes, and explicit cross-links. evaluated TreePlus by comparing it to a traditional node-link diagram in a controlled study and found that TreePlus outperforms the node-link layout for most tasks and is preferred by most participants. For a detailed discussion of the differences of TreePlus and Juniper, refer to . Most of these techniques, including Munzner's hyperbolic tree, the radial layouts, and TreePlus, also encode node attributes, but they limit attribute visualization to on-node encoding of one or few attributes.
@cite_41 @cite_19
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Despite extensive research, it is still difficult to produce effective interactive layouts for large graphs. Dense layout and occlusion make food Webs, ontologies and social networks difficult to understand and interact with. We propose a new interactive visual analytics component called TreePlus that is based on a tree-style layout. TreePlus reveals the missing graph structure with visualization and interaction while maintaining good readability. To support exploration of the local structure of the graph and gathering of information from the extensive reading of labels, we use a guiding metaphor of "plant a seed and watch it grow." It allows users to start with a node and expand the graph as needed, which complements the classic overview techniques that can be effective at (but often limited to) revealing clusters. We describe our design goals, describe the interface and report on a controlled user study with 28 participants comparing TreePlus with a traditional graph interface for six tasks. In general, the advantage of TreePlus over the traditional interface increased as the density of the displayed data increased. Participants also reported higher levels of confidence in their answers with TreePlus and most of them preferred TreePlus This paper presents work in progress on a new technique for visu- alising and manipulating large hierarchies. The information slices approach compactly visualises hierarchical structures using a series of semi-circular discs. The technique is described in the context of our early experience with a prototype file system visualiser based on information slices. Over the last few years, the emerging field of information visuali- sation has resulted in a numerous techniques for helping visualise and make sense of large information spaces (10). Among these are several techniques for visualising and interacting with large hierar- chies which go beyond the traditional approach taken in 2d scrolling browsers such as the Windows Explorer. Treemaps (6, 9) are space-filling visualisations of hierarchies based on successive horizontal and vertical subdivision of screen rectangles. The area of each rectangle is proportional to some at- tribute of the underlying hierarchy such as the (total) size of each subtree. Xdu (4) is a utility for the X window system which displays a graphical disk usage for Unix file systems. Rectangles are stacked from left to right as the directory tree is descended. The current di- rectory is represented by the leftmost rectangle, which is the entire height of the window. Subdirectories are represented by neighbour- ing rectangles in the next column, whose height are proportional to the size of each subdirectory. The hyperbolic browser (7) uses a focus and context technique based on hyperbolic geometry. A hierarchy is laid out uniformly on the hyperbolic plane and then mapped to the unit disc for display on screen. Nodes in the centre of the disc are largest and nodes are assigned progressively less space towards the perimeter of the disc. Cheops (3) is based on multiple re-use of overlaid triangles in the display. Working top-down, the selection of a node (triangle) at a particular level designates that node's children are to be represented 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.