While algorithms for planar graphs have received a lot of attention, few
papers have focused on the additional power that one gets from assuming an
embedding of the graph is available. While in the classic sequential setting,
this assumption gives no additional power (as a planar graph can be embedded in
linear time), we show that this is far from being the case in other settings.
We assume that the embedding is straight-line, but our methods also generalize
to non-straight-line embeddings. Specifically, we focus on sublinear-time
computation and massively parallel computation (MPC).
Our main technical contribution is a sublinear-time algorithm for computing a
relaxed version of an r-division. We then show how this can be used to
estimate Lipschitz additive graph parameters. This includes, for example, the
maximum matching, maximum independent set, or the minimum dominating set. We
also show how this can be used to solve some property testing problems with
respect to the vertex edit distance.
In the second part of our paper, we show an MPC algorithm that computes an
r-division of the input graph. We show how this can be used to solve various
classical graph problems with space per machine of O(n2/3+ϵ) for
some ϵ>0, and while performing O(1) rounds. This includes for
example approximate shortest paths or the minimum spanning tree. Our results
also imply an improved MPC algorithm for Euclidean minimum spanning tree