Recent works in graph visualization attempt to reduce the runtime ofrepulsionforce computation of force-directed algorithms using sampling. However, they fail to reduce the runtime forattractionforce computation to sublinear in the number of edges. We present theSubLinearForceframework for a fully sublinear-timeforce computationalgorithm for drawing large complex graphs. More precisely, we present new sublinear-time algorithms for theattraction forcecomputation of force-directed algorithms. We then integrate them with sublinear-time repulsion force computation to give a fully sublinear-time force computation. Extensive experiments show that our algorithms compute layouts on average 80% faster than the existing linear-time force computation algorithm, while obtaining significantly better quality metrics such as edge crossing and shape-based metrics.

This paper presents a new visual representation of graphs, inspired by the dot painting style of Central Australia. This painting style is established as a powerful medium for communicating information with abstraction, and has a long history of supporting storytelling.We propose a general framework GDot to visually represent in-formation as dot paintings. We describe computational techniques as well as the rendering effects to produce painterly representations of graphs and networks. We present visualization examples with various networks from diverse domains, from pure mathematics to social systems. Further, we briefly describe the extension of our dot painting visualization style to multi-dimensional data, dynamic data and geo-referenced data.

The multi-level graph drawing is a popular approach to visualize large and complex graphs. It recursively coarsens a graph and then uncoarsens the drawing using layout refinement. In this paper, we leverage the Louvain community detection algorithm for the multi-level graph drawing paradigm.More specifically, we present the Louvain-based multi-level graph drawing algorithm, and compare with other community detection algorithms such as Label Propagation and Infomap clustering. Experiments show that Louvain-based multi-level algorithm performs best in terms of efficiency (i.e., fastest runtime), while Label Propagation and Infomap-based multi-level algorithms perform better in terms of effectiveness (i.e., better visualization in quality metrics).

Recent works in graph visualization attempt to reduce the runtime of repulsion force computation of force-directed algorithms using sampling, however they fail to reduce the runtime for attraction force computation to sublinear in the number of edges.We present new sublinear-time algorithms for the attraction force computation of force-directed algorithms and integrate them with sublinear-time repulsion force computation.Extensive experiments show that our algorithms, operated as part of a fully sublinear-time force computation framework, compute graph layouts on average 80% faster than existing linear-time force computation algorithm, with surprisingly significantly better quality metrics on edge crossing and shape-based metrics.

Recent methods for visualizing graphs have used a map metaphor: vertices are represented as regions in the plane, and proximity between regions represents edges between vertices.In many real world applications, the data changes over time, resulting in a dynamic map. This paper introduces new methods for representing dynamic graphs with map animation. More specifically, we present three different animation methods: MDSV (Multidimensional scaling - Voronoi), TV (Tutte - Voronoi) and TD (Tutte - dual). These methods support operations such as addition and deletion of vertices and edges. Each of our methods uses a kind of matrix interpolation.

Abstract In this paper, we present a new framework for sublinear time force computation for visualization of big complex graphs. Our algorithm is based on the sampling of vertices for computing repulsion forces and edge sparsification for attraction force computation. More specifically, for vertex sampling, we present three types of sampling algorithms, including random sampling, geometric sampling, and combinatorial sampling, to reduce the repulsion force computation to sublinear in the number of vertices. We utilize a spectral sparsification approach to reduce the number of attraction force computations to sublinear in the number of edges for dense graphs. We also present a smart initialization method based on radial tree drawing of the BFS spanning tree rooted at the center. Experiments show that our new sublinear time force computation algorithms run quite fast, while producing good visualization of large and complex networks, with significant improvements in quality metrics such as shape‐based and edge crossing metrics.