  Hyperbolic geometry offers a natural focus + context for data visualization
and has been shown to underlie real-world complex networks. However, current
hyperbolic network visualization approaches are limited to special types of
networks and do not scale to large datasets. With this in mind, we designed,
implemented, and analyzed three methods for hyperbolic visualization of
networks in the browser based on inverse projections, generalized
force-directed algorithms, and hyperbolic multi-dimensional scaling (H-MDS). A
comparison with Euclidean MDS shows that H-MDS produces embeddings with lower
distortion for several types of networks. All three methods can handle
node-link representations and are available in fully functional web-based
systems.
