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 structures, while others attempt to preserve local
neighborhoods. Few methods attempt to do both, and it is not always possible to
capture well both local and global information in two dimensions, which is
where most graph drawing live. The choice of using a local or a global
embedding for visualization depends not only on the task but also on the
structure of the underlying data, which may not be known in advance. For a
given graph, LGS aims to find a good balance between the local and global
structure to preserve. We evaluate the performance of LGS with synthetic and
real-world datasets and our results indicate that it is competitive with the
state-of-the-art methods, using established quality metrics such as stress and
neighborhood preservation. We introduce a novel quality metric, cluster
distance preservation, to assess intermediate structure capture. All
source-code, datasets, experiments and analysis are available online.Comment: Appears in the Proceedings of the 31st International Symposium on
Graph Drawing and Network Visualization (GD 2023