Visualizing graphs and networks in non‐Euclidean space can have benefits such as natural
focus+ context in hyperbolic space and the familiarity of interactions in spherical space …

We describe an efficient and scalable spherical graph embedding method. The method uses
a generalization of the Euclidean stress function for Multi-Dimensional Scaling adapted to …

The need to understand the structure of hierarchical or high-dimensional data is present in a
variety of fields. Hyperbolic spaces have proven to be an important tool for embedding …

We present a method for balancing between the Local and Global Structures (LGS) in graph
embedding, via a tunable parameter. Some embedding methods aim to capture global …

Stress models are a promising approach for graph drawing. They minimize the weighted
sum of the squared errors of the Euclidean and desired distances for each node pair. The …

There exists many algorithms to compute node-link diagrams for graph visualization. Almost
all of these algorithms aim to draw the graph in the flat Euclidean plane, but there are …

Today, real-world entities are becoming increasingly interconnected (eg, individuals
interacting on social platforms). Nevertheless, these entities and their interconnectivity …

Recent research revealed that embedding a node-link diagram in a flat torus avoids edge
crossings, improves aesthetic metrics, and has advantages in tasks such as path tracking …