Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Graph Neural Networks Mostly Smooth Data — What That Means

People working with networks found that graph neural networks often do something simple: they smooth the information across nodes, like a mild blur, not reinventing features.
Many benchmark datasets already have strong feature vectors, so the network mostly helps to denoise or tidy up messy signals.
It turns out the math behind these methods acts similar to low-pass filtering, meaning high-frequency noise is reduced and the clear parts stay.

This is surprising if you expected deep models to discover brand-new patterns.
Instead they pass signals around neighbors and average, that helps when data was noisy but it wont create new facts.
The good news, simple tweaks can make them more stable against noise, and new designs may focus on leaving useful details alone.
If you use them remember: they are great at smoothing, less great at inventing, so pick your data and goals wisely.

Read article comprehensive review in Paperium.net:
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

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