I am solving a graph-level problem. I want to fit graph embeddings to a learn-to-rank NN to rank the graphs by their "quality". The "quality" of the graphs is determined by whether they have certain shape or structure, say they have self-loops and no loose end, has many split nodes and merging nodes etc. The node and edge features are not in consideration.

To my understanding, graph embeddings are best suited for graph similarity comparison, are there any techniques that can fit my use case?

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Weary-Marionberry-15 t1_j06hbfh wrote on December 14, 2022 at 12:36 PM

Is your “quality” known for each graph? If so, I recon you could do this by simply building a convolutional graph neural network and phrase this as a regression problem (assuming quality is a float).

J00Nnn OP t1_j09l1ov wrote on December 15, 2022 at 1:24 AM

Thanks for replying! The quality of the graph is known in pairs like this: {G1 > G2, G3 > G4}, I'm not sure if that is suitable for convolutional GNN.

afireohno t1_j0aqu8w wrote on December 15, 2022 at 7:42 AM

Use a pairwise loss.

Weary-Marionberry-15 t1_j0azzw2 wrote on December 15, 2022 at 9:48 AM

Could you elaborate on what this pair is? It’s not clear to me what the entries are.

KlutzyLeadership3652 t1_j07w3yc wrote on December 14, 2022 at 6:35 PM

Graph Matching Networks could be a good starting point. As another comment said, you do need to have some supervision (annotated graphs).

J00Nnn OP t1_j09l1n7 wrote on December 15, 2022 at 1:24 AM

Thanks! I will take a look at the papers.