Memory and Capacity of Graph Embedding Methods. (arXiv:2208.08769v1 [stat.ML])

We introduce a method for embedding graphs as vectors in a
structure-preserving manner. In this paper, we showcase its rich
representational capacity and give some theoretical properties of our method.
In particular, our procedure falls under the bind-and-sum approach, and we show
that our binding operation - the tensor product - is the most general binding
operation that respects the principle of superposition. Similarly, we show that
the spherical code achieves optimal compression. We then establish some precise
results characterizing …

arxiv capacity embedding graph memory ml