Post #94

This first X (which is in latex \mathcal{X}) is the set of all input features. What this theorem says is that you need to have input features that are countable. Countable means rational or categorical, but not irrational numbers. So this condition "prohibits" any "continuous" features such as real numbers (e.g. degrees). If your set of features is uncountable then the sum is not injective anymore and hence GNN becomes less powerful than 1-WL test. So that's why if you use one-hot encoding of features your GNN becomes theoretically more powerful.