Somewhere in my HN history is this same question and I can't say I've got a conclusive answer. My partially confident takeaway is that GNNs describe the architecture of the neural network itself, much in the same way that convolutional or recurrent are terms used to describe other network architectures.
There are two confusing parts for me
1 - The words network and graph are nearly synonymous in this context, and IIRC most neural network architectures are actually graphs that fit some specific pattern. I don't know what makes a 'graph neural network' special (my guess is it has to do with how the layers relate but i don't know)
2 - I almost always see a mention of a graph-related use cases in the context of GNNs. I don't know if there is a fundamental reason for that or if it just so happens that people who have huge graphs worth applying ML to are actually just have really good intuition about how graphs can be leveraged and go that route.
There are two confusing parts for me
1 - The words network and graph are nearly synonymous in this context, and IIRC most neural network architectures are actually graphs that fit some specific pattern. I don't know what makes a 'graph neural network' special (my guess is it has to do with how the layers relate but i don't know)
2 - I almost always see a mention of a graph-related use cases in the context of GNNs. I don't know if there is a fundamental reason for that or if it just so happens that people who have huge graphs worth applying ML to are actually just have really good intuition about how graphs can be leveraged and go that route.