A multi-level middle-out cross-zooming approach for large graph analytics

Wong, Pak Chung; Mackey, Patrick; Cook, Kristin A.; Rohrer, Randall M.; Foote, H.P.; Whiting, Mark

doi:10.1109/vast.2009.5333880

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…To begin, we need a scalable visual analytics platform but there are very few tools designed for large-scale graph exploration. The GreenHornet 26 visual analytics tool is designed for scalability and utilizes a hierarchical partitioning of the graph, exemplifying the importance of data representation. The data hierarchy in GreenHornet is a sequence of increasingly coarser partitions of the graph, generated by merging vertices of lowest degree to their neighbors.…”

Section: Visualizing Trillions Of Trianglesmentioning

confidence: 99%

Graphing trillions of triangles

Burkhardt

2016

Information Visualization

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The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can be many times larger than the input. This is true for many graph problems, such as listing all triangles in a graph. Enabling scalable graph exploration for Big Graphs requires new approaches to algorithms, architectures, and visual analytics. A brief tutorial is given to aid the argument for thoughtful representation of data in the context of graph analysis. Then a new algebraic method to reduce the arithmetic operations in counting and listing triangles in graphs is introduced. Additionally, a scalable triangle listing algorithm in the MapReduce model will be presented followed by a description of the experiments with that algorithm that led to the current largest and fastest triangle listing benchmarks to date. Finally, a method for identifying triangles in new visual graph exploration technologies is proposed.

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Section: Visualizing Trillions Of Trianglesmentioning

confidence: 99%

Graphing trillions of triangles

Burkhardt

2016

Information Visualization

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“…In this paper, we focus on the visual analysis of big graphs (networks) and their anomalies [14,15]. Previous literature on visualizing large graphs falls into three categories.…”

Section: Visualization Challenges For Big Data and Related Workmentioning

confidence: 99%

Visual analysis of large-scale network anomalies

2013

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The amount of information flowing across communication networks has rapidly increased. The highly dynamic and complex networks, represented as large graphs, make the analysis of such networks increasingly challenging. In this paper, we provide a brief overview of several useful visualization techniques for the analysis of spatiotemporal anomalies in large-scale networks. We make use of community-based similarity graphs (CSGs), temporal expansion model graphs (TEMGs), correlation graphs (CGs), high-dimension projection graphs (HDPGs), and topology-preserving compressed graphs (TPCGs). CSG is used to detect anomalies based on community membership changes rather than individual nodes and edges and therefore may be more tolerant to the highly dynamic nature of large networks. TEMG transforms network topologies into directed trees so that efficient search is more likely to be performed for anomalous changes in network behavior and routing topology in large dynamic networks. CG and HDPG are used to examine the complex relationship of data dimensions among graph nodes through transformation in a high-dimensional space. TPCG groups nodes with similar neighbor sets into mega-nodes, thus making graph visualization and analysis more scalable to large networks. All the methods target efficient large-graph anomaly visualization from different perspectives and together provide valuable insights.

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“…38 More sophisticated interactive graph visualizations provide an integrated representation of details and overview. Research in this area includes the work on subgraph discovery by Faloutsos et al, 14 the interaction techniques for subgraph selection and manipulation by McGuffin and Jurisica, 16 the work on sigma lenses by Pietriga et al, 17 and research on fisheye view by Schaffer et al 18 and Gansner et al 15 Relevant work on visual exploration of large graphs also includes the combination of semantic and geometric distortions in the visualization system by van Ham and van Wijk, 19 the cross-zooming approach by Wong et al, 21 and the "search, show context, expand on demand" model by van Ham and Perer. 20 Multiple coordinated views, supporting investigation with distinct views showing different aspects of the same conceptual entity, have been used as well.…”

Section: Graph Navigation and Explorationmentioning

confidence: 99%

“…[3][4][5][6][7][8] Several techniques and different representations have been developed to improve understandability, 2, 9-13 and enabled an analyst to explore the graph and access low level information. [14][15][16][17][18][19][20][21] Many researchers have investigated approaches for efficient representation of multi-dimensional data. [22][23][24][25][26][27] Multi-focus interaction [28][29][30] and the use of multiple coordinated views [31][32][33][34][35] to facilitate comparison have been studied as well.…”

Section: Introductionmentioning

confidence: 99%

Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data

2013

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Information-rich data sets bring several challenges in the areas of visualization and analysis, even when associated with node-link network visualizations. This paper presents an integration of multi-focus and multi-level techniques that enable interactive, multi-step comparisons in node-link networks. We describe NetEx, a visualization tool that enables users to simultaneously explore different parts of a network and its thematic data, such as time series or conditional probability tables. NetEx, implemented as a Cytoscape plug-in, has been applied to the analysis of electrical power networks, Bayesian networks, and the Enron e-mail repository. In this paper we briefly discuss visualization and analysis of the Enron social network, but focus on data from an electrical power network. Specifically, we demonstrate how NetEx supports the analytical task of electrical power system fault diagnosis. Results from a user study with 25 subjects suggest that NetEx enables more accurate isolation of complex faults compared to an especially designed software tool.

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A multi-level middle-out cross-zooming approach for large graph analytics

Cited by 12 publications

References 21 publications

Graphing trillions of triangles

Graphing trillions of triangles

Visual analysis of large-scale network anomalies

Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data

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