TreePlus: Interactive Exploration of Networks with Enhanced Tree Layouts

Lee, B.; Parr, Cynthia; Plaisant, Catherine; Bederson, Benjamin B.; Veksler, Vladislav D.; Gray, Wayne D.; Kotfila, C.

doi:10.1109/tvcg.2006.106

Cited by 76 publications

(13 citation statements)

References 33 publications

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“…The works most closely related to ours are TreePlus by Lee et al [32] and the application-specific variant of TreePlus, GOTreePlus [30]. TreePlus introduces the "plant a seed and watch it grow" principle.…”

Section: Tree-based Graph Visualizationmentioning

confidence: 99%

“…A common strategy to explore large graphs is a bottom-up approach, where the analysis begins with a search or a query, and then more context is added as needed [49,50]. Flavors of this approach range from explicitly revealing neighborhoods of nodes [18,32], to querying for paths or connectivity in a network [27,40], to querying based on a degree-of-interest function [49], to associative browsing and complex queries [25,46]. All these examples are designed to return or expand a single subgraph, in contrast to techniques such as VIGOR [42] that are used to analyze (typically structural) queries that return many different subgraphs.…”

Section: Query-based Visualization Of Large Graphsmentioning

confidence: 99%

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Juniper: A Tree+Table Approach to Multivariate Graph Visualization

Nobre

Streit

Lex

2019

IEEE Trans. Visual. Comput. Graphics

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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.

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Section: Tree-based Graph Visualizationmentioning

confidence: 99%

Section: Query-based Visualization Of Large Graphsmentioning

confidence: 99%

Juniper: A Tree+Table Approach to Multivariate Graph Visualization

Nobre

Streit

Lex

2019

IEEE Trans. Visual. Comput. Graphics

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“…In bottom-up approaches, the exploration starts with a small subset of a graph that can then be expanded. For example, in Treeplus [LPP* 06a] a graph can be continuously expanded in a flexible tree layout, starting from a seed node. Ham and Perer [vHP09], Abello et al [AHSS13], and Luger et al [LSG* 15] use more sophisticated methods to select and expand graph subsets based on degree of interest functions.…”

Section: Related Workmentioning

confidence: 99%

Pathfinder: Visual Analysis of Paths in Graphs

Partl

Gratzl

Streit

et al. 2016

Computer Graphics Forum

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The analysis of paths in graphs is highly relevant in many domains. Typically, path-related tasks are performed in node-link layouts. Unfortunately, graph layouts often do not scale to the size of many real world networks. Also, many networks are multivariate, i.e., contain rich attribute sets associated with the nodes and edges. These attributes are often critical in judging paths, but directly visualizing attributes in a graph layout exacerbates the scalability problem. In this paper, we present visual analysis solutions dedicated to path-related tasks in large and highly multivariate graphs. We show that by focusing on paths, we can address the scalability problem of multivariate graph visualization, equipping analysts with a powerful tool to explore large graphs. We introduce Pathfinder (Figure 1), a technique that provides visual methods to query paths, while considering various constraints. The resulting set of paths is visualized in both a ranked list and as a node-link diagram. For the paths in the list, we display rich attribute data associated with nodes and edges, and the node-link diagram provides topological context. The paths can be ranked based on topological properties, such as path length or average node degree, and scores derived from attribute data. Pathfinder is designed to scale to graphs with tens of thousands of nodes and edges by employing strategies such as incremental query results. We demonstrate Pathfinder's fitness for use in scenarios with data from a coauthor network and biological pathways.

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“…Previous research showed that querying tools greatly benefit from having intuitive visual user interfaces, through which the user can more naturally specify queries and understand the resulting matches [5, 8, 12]. …”

Section: Our Completed Work and Related Workmentioning

confidence: 99%

“…This points to the need for intuitive visual user interfaces that helps the user to both specify their queries and visualize the matches, as such interfaces are easier to use and can help the user understand the results [5, 8, 12]. …”

Section: Introductionmentioning

confidence: 99%

Interactive Querying over Large Network Data

Pienta

Tamersoy

Tong

et al. 2015

Companion Proceedings of the 20th International Conference on Intelligent User Interfaces

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Given the explosive growth of modern graph data, new methods are needed that allow for the querying of complex graph structures without the need of a complicated querying languages; in short, interactive graph querying is desirable. We describe our work towards achieving our overall research goal of designing and developing an interactive querying system for large network data. We focus on three critical aspects: scalable data mining algorithms, graph visualization, and interaction design. We have already completed an approximate subgraph matching system called MAGE in our previous work that fulfills the algorithmic foundation allowing us to query a graph with hundreds of millions of edges. Our preliminary work on visual graph querying, Graphite, was the first step in the process to making an interactive graph querying system. We are in the process of designing the graph visualization and robust interaction needed to make truly interactive graph querying a reality.

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TreePlus: Interactive Exploration of Networks with Enhanced Tree Layouts

Cited by 76 publications

References 33 publications

Juniper: A Tree+Table Approach to Multivariate Graph Visualization

Juniper: A Tree+Table Approach to Multivariate Graph Visualization

Pathfinder: Visual Analysis of Paths in Graphs

Interactive Querying over Large Network Data

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