You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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

[2]: Summary: We developed an interactive GO (gene ontology) browser named GOTreePlus that superimposes annotation information over GO structures. It can facilitate the identification of important GO terms through interactive visualization of them in the GO structure. The interactive pie chart summarizing an annotation distribution for a selected GO term provides users with a succinct context-sensitive overview of their experimental results. We tested our GOTreePlus using a proteome profiling dataset obtained on differentiation of retinal pigment epithelial cells where 399 proteins were quantified. Availability: http: bioinformatics.cnmcresearch.org GOTreePlus Contact: bongshin@microsoft.com or jseo@cnmcresearch.org
The works most closely related to ours are TreePlus by [1] and the application-specific variant of TreePlus, GOTreePlus [2] . 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.