ASK-GraphView: A Large Scale Graph Visualization System

Abello, James; Ham, F. van; Krishnan, Neeraj

doi:10.1109/tvcg.2006.120

Cited by 237 publications

(203 citation statements)

References 22 publications

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“…c [1,3,4]). The main idea of this approach is to detect interesting or typical topological subsustructures in the network and then to draw them using an appropriate algorithm.…”

Section: Topologies Detection Based Approachmentioning

confidence: 99%

“…Another approach consists in representing the graph as a matrix and using linear algebra techniques ( [18,27]). Finally, a recent technique is based on topologies detection and the use of an appropriate algorithm for each topology ( [1,4,3]). …”

Section: Related Workmentioning

confidence: 99%

“…In [1], Abello et al present an algorithm that first search for the peripheral forest (i.e. proper subgraphs that are trees) using a technique called pilling and collapse them into single nodes.…”

Section: Topologies Detection Based Approachmentioning

confidence: 99%

“…In the next step, the algorithm search for biconnected components of the resulting graph. Finally, if some biconnected components are "too" large (for more detail see [1]), they try to cluster it using MCL algorithm of [26]. In [1], Abello et al also present the steerable exploration tool where they integrated this algorithm.…”

Section: Topologies Detection Based Approachmentioning

confidence: 99%

See 3 more Smart Citations

Large Quasi-Tree Drawing: A Neighborhood Based Approach

Bourqui

2009

2009 13th International Conference Information Visualisation

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“…c [1,3,4]). The main idea of this approach is to detect interesting or typical topological subsustructures in the network and then to draw them using an appropriate algorithm.…”

Section: Topologies Detection Based Approachmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Topologies Detection Based Approachmentioning

confidence: 99%

Section: Topologies Detection Based Approachmentioning

confidence: 99%

See 2 more Smart Citations

Large Quasi-Tree Drawing: A Neighborhood Based Approach

Bourqui

2009

2009 13th International Conference Information Visualisation

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“…More interactive tools can now perform incremental data mining techniques during the user's interaction latency [31]. And some are integrating data-centric approahces such as pre-computation, approximation, and early algorithm termination, by making the systems aware of the I/O and screen bottlenecks and by the careful adoption of such new algorithms [60], [61], [30].…”

Section: B Scaling Up Graph Sensemaking: Interactive Sensemaking and Smentioning

confidence: 99%

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Pienta

Abello

Kahng

et al. 2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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Abstract-Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant works from multiple research fields -data mining, machine learning, human-computer ineraction, information visualization, information retrieval, and recommender systems -to underline their parallel and complementary contributions to graph sensemaking.We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global). We summarize and compare their strengths and weaknesses, and highlight open challenges. We conclude with future research directions for graph sensemaking.

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Visualizing large graphs

Shi

2015

WIREs Computational Stats

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With the prevalence of big data, there is a growing need for algorithms and techniques for visualizing very large and complex graphs. In this article, we review layout algorithms and interactive exploration techniques for large graphs. In addition, we briefly look at softwares and datasets for visualization graphs, as well as challenges that need to be addressed. WIREs Comput Stat 2015, 7:115–136. doi: 10.1002/wics.1343 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Data: Types and Structure > Graph and Network Data Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization

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ASK-GraphView: A Large Scale Graph Visualization System

Cited by 237 publications

References 22 publications

Large Quasi-Tree Drawing: A Neighborhood Based Approach

Large Quasi-Tree Drawing: A Neighborhood Based Approach

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Visualizing large graphs

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