3 research outputs found
Treebar Maps: Schematic Representation of Networks at Scale
Many data sets, crucial for today's applications, consist essentially of
enormous networks, containing millions or even billions of elements. Having the
possibility of visualizing such networks is of paramount importance. We propose
an algorithmic framework and a visual metaphor, dubbed treebar map, to provide
schematic representations of huge networks. Our goal is to convey the main
features of the network's inner structure in a straightforward,
two-dimensional, one-page drawing. This drawing effectively captures the
essential quantitative information about the network's main components. Our
experiments show that we are able to create such representations in a few
hundreds of seconds. We demonstrate the metaphor's efficacy through visual
examination of extensive graphs, highlighting how their diverse structures are
instantly comprehensible via their representations.Comment: 27 pages, 32 figures, 1 tabl
Spectrum-preserving sparsification for visualization of big graphs
We present a novel spectrum-preserving sparsification algorithm for visualizing big graph data. Although spectral methods have many advantages, the high memory and computation costs due to the involved Laplacian eigenvalue problems could immediately hinder their applications in big graph analytics. In this paper, we introduce a practically efficient, nearly-linear time spectral sparsification algorithm for tackling real-world big graph data. Besides spectral sparsification, we further propose a node reduction scheme based on intrinsic spectral graph properties to allow more aggressive, level-of-detail simplification. To enable effective visual exploration of the resulting spectrally sparsified graphs, we implement spectral clustering and edge bundling. Our framework does not depend on a particular graph layout and can be integrated into different graph drawing algorithms. We experiment with publicly available graph data of different sizes and characteristics to demonstrate the efficiency and effectiveness of our approach. To further verify our solution, we quantitatively compare our method against different graph simplification solutions using a proxy quality metric and statistical properties of the graphs