CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with
  GNNs
Florian Grötschla Joël Mathys Robert Veres Roger Wattenhofer
Graph Visualization, also known as Graph Drawing, aims to find geometric
embeddings of graphs that optimize certain criteria. Stress is a widely used
metric; stress is minimized when every pair of nodes is positioned at their
shortest path distance. However, stress optimization presents computational
challenges due to its inherent complexity and is usually solved using
heuristics in practice. We introduce a scalable Graph Neural Network (GNN)
based Graph Drawing framework with sub-quadratic runtime that can learn to
optimize stress. Inspired by classical stress optimization techniques and
force-directed layout algorithms, we create a coarsening hierarchy for the
input graph. Beginning at the coarsest level, we iteratively refine and
un-coarsen the layout, until we generate an embedding for the original graph.
To enhance information propagation within the network, we propose a novel
positional rewiring technique based on intermediate node positions. Our
empirical evaluation demonstrates that the framework achieves state-of-the-art
performance while remaining scalable.