Abstract: Systems and methods for graph model search and/or for architecture insight can include training and testing a plurality of graph models. For example, the systems and methods can generate a plurality of synthetic graph datasets, which can then be utilized to train a plurality of graph models with varying graph model architectures. The trained graph models can then be evaluated based on outputs generated by the models based on test inputs. The evaluation data can then be utilized for providing particular graph model insight and/or may be utilized to enable task-specific graph model search.

Abstract: The present disclosure provides a neural graph embedding approach that embeds topology and metadata information in separate metric spaces. In particular, even using models with explicit metadata embeddings, topology embeddings become correlated with the metadata when the metadata are related to the graph structure. To prevent this information leakage, the present disclosure introduces a Metadata-Orthogonal Node Embedding Training (MONET) unit, which trains the topology embeddings on a hyperplane orthogonal to the metadata embeddings.