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Visualizing large graphs

Advanced Review

Yifan Hu, Lei Shi

Published Online: Jan 12 2015

DOI: 10.1002/wics.1343

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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

Visualization of a small social network.

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Visualizations of an airline route graph. Left: Original layout. Right: Geometric edge bundling.

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Visualizations of a protein interaction graph: (a) Unsimplified; (b) Stochastic edge sampling; (c) Geodesic clustering; (d) Edge centrality based filtering.

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Large graph exploration through Degree‐of‐Interest diffusion. The data set is US legal document citation network.

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The five types of hierarchical abstraction based visualization. The interactive navigation methods on large graph visualization normally change the hierarchy setting within each type or switch between different types.

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Semantic and heterogeneous abstraction of large graphs. Left: PivotGraph of a communication network of people in a large company, x‐axis is division, y‐axis is office geography.¹⁰⁷ Right: OnionGraph of a host/domain‐user‐application network in a commodity Ethernet setting.

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Large graph visualizations through node clustering.,,,

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Motif simplification of a network of wiki edits.

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Lossless compression a small network traffic graph.

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Example drawings of large graphs.

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Result of some of the algorithms applied to two graphs, dw256A and qh882.

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An illustration of the edge collapsing‐based graph coarsening. Left: original graph with 85 vertices. Edges in a maximal independent edge set are thickened. Right: a coarser graph with 43 vertices resulted from coalescing thickened edges.

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An illustration of the quadtree data structure. Left: the overall quadtree. Right: supernodes with reference to a vertex at the top middle part of the graph, with θ = 1.

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