Are there methods to represent entire knowledge graphs using a single embedding vector?

Question

In a knowledge graph, embedding vectors can be learned for nodes (node embedding) and edges (edge embeddings). Is there a method to learn one single embedding vector for the entire knowledge graph?

Answer 1

The answer is Graph Readout operation can get a graph level representation out of the node/edge representations. Read the following: https://lifesci.dgl.ai/api/model.readout.html