Think of it as an ensemble for blending your normal NN features (ex: RNN for time/clickstreams) with a model that can also leverage useful graph features (document citations, app logins, chemicals connecting, social graphs).
We think a lot about security/fraud and digital journeys, where NN + xgboost are popular in general, and graph is used seperately (or upstream) for looking at broader structure. GNNs help blend these models. For example, in analyzing malicious user accounts (ex: misinfo on twitter), we already get many time/nlp/etc scores for whatever events/entities we look at, and use the social network structure to ensure better propagation/blending, similar to why boosting and ensemble methods became popular to beginwith. Feel free to DM if interested, we are quite excited by this space and working on some things here.
We think a lot about security/fraud and digital journeys, where NN + xgboost are popular in general, and graph is used seperately (or upstream) for looking at broader structure. GNNs help blend these models. For example, in analyzing malicious user accounts (ex: misinfo on twitter), we already get many time/nlp/etc scores for whatever events/entities we look at, and use the social network structure to ensure better propagation/blending, similar to why boosting and ensemble methods became popular to beginwith. Feel free to DM if interested, we are quite excited by this space and working on some things here.