Abstract
Sensemaking of large graphs, specifically those with millions of nodes, is a crucial task in many fields. Automatic graph layout algorithms, augmented with real-time human-in-the-loop interaction, can potentially support sensemaking of large graphs. However, designing interactive algorithms to achieve this is challenging. In this paper, we tackle the scalability problem of interactive layout of large graphs, and contribute a new GPU-based force-directed layout algorithm that exploits graph topology. This algorithm can interactively layout graphs with millions of nodes, and support real-time interaction to explore alternative graph layouts. Users can directly manipulate the layout of vertices in a force-directed fashion. The complexity of traditional repulsive force computation is reduced by approximating calculations based on the hierarchical structure of multi-level clustered graphs. We evaluate the algorithm performance, and demonstrate human-in-the-loop layout in two sensemaking case studies. Moreover, we summarize lessons learned for designing interactive large graph layout algorithms on the GPU.
Highlights
Graphs are commonly used to depict complex relations among objects
We propose our above approximated repulsive force computation algorithm to weave into the multi-level paradigm for big graph layout
We tested our algorithm on a desktop computer running Windows 7 Enterprise, which was equipped with an Intel i7 processor and an NVIDIA GeForce GTX 680 graphics card programmed with CUDA 7.5
Summary
Graphs are commonly used to depict complex relations among objects. Graph drawing offers solutions to geometrically represent graphs, with the intention of improving their readability.This supports applications and analysis in various domains, such as social network analysis (e.g., [1,2]), cyber security (e.g., [3]), and intelligence analysis (e.g., [4,5]).with the increasing size and complexity of graph data, the performance of drawing large graphs, especially those with millions of nodes, is still a significant challenge. Graph drawing offers solutions to geometrically represent graphs, with the intention of improving their readability This supports applications and analysis in various domains, such as social network analysis (e.g., [1,2]), cyber security (e.g., [3]), and intelligence analysis (e.g., [4,5]). Existing large graph layout algorithms emphasize static graph layout results either based on their structures [9] or semantic meanings [10] They focus on generating static and visually pleasing results, but their layouts are constrained by their predefined aims. These algorithms are limited in their support for interactive sensemaking tasks with large graphs
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