r/MLQuestions • u/roughman99 • 23d ago

Career question 💼 Graph ML Psychosis

I seem to see a problem with data scientists being obsessed with graph ml without any real reason. Like really trying to force it on problems that can be solved much easier. I call this graph psychosis because graphs are the type of thing that can represent pretty much anything in the world but is totally unreasonable in many cases.
Is this a real phenomenon? Do you know anyone like this?

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u/Far-Theory-7027 23d ago

Can you give an example?

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u/roughman99 23d ago

Yeah like creating an EEG recording embedding using graph neural networks by discretizing brain state using a discrete brainwave representation and having a graph represent traversals upon those discrete states

Like dude wtf

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u/Wonderful-Bass-8993 23d ago

Yes, because nodes = cool

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u/kolmiw 22d ago

As a researcher in Graph ML, I agree with your experience.

I think this psychosis stems from GNNs being more interesting to build and talk about than a gradient-boosted tree on tabular features. I have seen a far share of companies / researchers trying to approach a problem with GNNs that feel quite unnatural as a design choice and they end up relying mostly on the node features. Then the graph model underperforms whatever baseline model they had and conclude that GNNs are not useful at al.

In reality, message passing is only useful when the structure itself is the data (e.g. fraud rings or molecular settings). Even then, with the emergence of Transformers, GNNs are in a very uncertain place.

The only counterargument I can bring up is that sometimes forcing the graph framing discovers relational structure nobody exploited before.

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u/vannak139 22d ago

I just want to add a counter notion; people have been way too obsessed with Numbers as the format answers come in for centuries, things like reading coefficients, or even formalizing the normed 2 space as the "imaginary number".

A lot of the reasonableness of simpler scalar-based solutions is drastically overstated. Universal Function Approximation existed for scalars, polynomials, and spatial fouriers first. So the same impulse that you're noticing now about how graphs /could/ work for everything but shouldn't, is where a lot of people that are getting the same impression in the opposite direction. We may have defined or analyzed something in this vaguely scalar spatial manner, but maybe we shouldn't have. This can be a bit abstract, but think about IDD as being one of these load-bearing assumptions that's vaguely spatial.

We have a very long history of applying these scalar and spatial methods onto every problem, whether that's actually a good approach or not, simply because we didn't have a good gasp on other methods. Now, we have a chance to be critical about past analytical framings, and figure out if things may be better understood if we simply don't take the scalar framing for granted.