[D] State-of-the-art permutation-invariant graph embeddings

Suppose I have a data set consisting of weighted undirected simple graphs. I would like to learn a vector representation of these graphs. What are the state-of-the-art (2022) architectures/methods for learning such representations? Ideally, the representations are permutation-invariant. For what it's worth, I am only interested in the case where graphs (vertices, edges, and their respective weights) are fully observed; I'm not interested cases unobserved nodes.

An additional requirement is the embedding must have a lower dimension that the number …

art graph machinelearning state