Graph neural networks: a review of methods and applications

It's another graph neural networks survey paper today! Clearly, this covers much of the same territory as we looked at earlier in the week, but when we're lucky enough to get two surveys published in short succession it can add a lot to compare the two different perspectives and sense of what's important. In particular here, Zhou et al., have a different formulation for describing the core GNN problem, and a nice approach to splitting out the various components. Rather than make this a standalone write-up, I'm going to lean heavily on the Graph neural network survey we looked at on Wednesday and try to enrich my understanding starting from there. For this survey, the GNN problem is framed based on the formulation in the original GNN paper, 'The graph neural network model,' Scarselli 2009.